Overview

Dataset statistics

Number of variables33
Number of observations3182
Missing cells2348
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory820.5 KiB
Average record size in memory264.0 B

Variable types

Categorical6
Numeric27

Alerts

bill.id has a high cardinality: 3182 distinct valuesHigh cardinality
date has a high cardinality: 1291 distinct valuesHigh cardinality
bill.str has a high cardinality: 3156 distinct valuesHigh cardinality
bill.desc has a high cardinality: 2691 distinct valuesHigh cardinality
sponsors has a high cardinality: 616 distinct valuesHigh cardinality
cosponsors has a high cardinality: 1593 distinct valuesHigh cardinality
year is highly overall correlated with congnoHigh correlation
congno is highly overall correlated with yearHigh correlation
tw.latent1 is highly overall correlated with tw.law.courts.and.judgesHigh correlation
tw.abortion.and.social.conservatism is highly overall correlated with tw.civil.rights and 5 other fieldsHigh correlation
tw.agriculture is highly overall correlated with tw.education and 7 other fieldsHigh correlation
tw.banking.and.finance is highly overall correlated with tw.economy and 1 other fieldsHigh correlation
tw.civil.rights is highly overall correlated with tw.abortion.and.social.conservatism and 5 other fieldsHigh correlation
tw.congress.and.procedural is highly overall correlated with tw.crime and 2 other fieldsHigh correlation
tw.crime is highly overall correlated with tw.abortion.and.social.conservatism and 5 other fieldsHigh correlation
tw.defense.and.foreign.policy is highly overall correlated with tw.civil.rightsHigh correlation
tw.economy is highly overall correlated with tw.banking.and.finance and 4 other fieldsHigh correlation
tw.education is highly overall correlated with tw.agriculture and 3 other fieldsHigh correlation
tw.energy is highly overall correlated with tw.agriculture and 3 other fieldsHigh correlation
tw.environment is highly overall correlated with tw.agriculture and 3 other fieldsHigh correlation
tw.fair.elections is highly overall correlated with tw.womens.issuesHigh correlation
tw.federal.agencies.and.gov.regulation is highly overall correlated with tw.congress.and.procedural and 3 other fieldsHigh correlation
tw.higher.education is highly overall correlated with tw.agriculture and 1 other fieldsHigh correlation
tw.immigration is highly overall correlated with tw.abortion.and.social.conservatism and 4 other fieldsHigh correlation
tw.indian.affairs is highly overall correlated with tw.abortion.and.social.conservatism and 4 other fieldsHigh correlation
tw.intelligence.and.surveillance is highly overall correlated with tw.abortion.and.social.conservatism and 7 other fieldsHigh correlation
tw.labor is highly overall correlated with tw.agriculture and 3 other fieldsHigh correlation
tw.law.courts.and.judges is highly overall correlated with tw.latent1High correlation
tw.transportation is highly overall correlated with tw.agriculture and 4 other fieldsHigh correlation
tw.veterans.affairs is highly overall correlated with tw.indian.affairs and 1 other fieldsHigh correlation
tw.womens.issues is highly overall correlated with tw.abortion.and.social.conservatism and 10 other fieldsHigh correlation
bill.desc has 397 (12.5%) missing valuesMissing
sponsors has 416 (13.1%) missing valuesMissing
cosponsors has 1535 (48.2%) missing valuesMissing
bill.id is uniformly distributedUniform
bill.str is uniformly distributedUniform
bill.desc is uniformly distributedUniform
cosponsors is uniformly distributedUniform
bill.id has unique valuesUnique
tw.latent1 has 191 (6.0%) zerosZeros
tw.abortion.and.social.conservatism has 2616 (82.2%) zerosZeros
tw.agriculture has 2701 (84.9%) zerosZeros
tw.banking.and.finance has 2377 (74.7%) zerosZeros
tw.civil.rights has 2400 (75.4%) zerosZeros
tw.congress.and.procedural has 1682 (52.9%) zerosZeros
tw.crime has 2229 (70.1%) zerosZeros
tw.defense.and.foreign.policy has 2181 (68.5%) zerosZeros
tw.economy has 1647 (51.8%) zerosZeros
tw.education has 2452 (77.1%) zerosZeros
tw.energy has 2605 (81.9%) zerosZeros
tw.environment has 2540 (79.8%) zerosZeros
tw.fair.elections has 2898 (91.1%) zerosZeros
tw.federal.agencies.and.gov.regulation has 1598 (50.2%) zerosZeros
tw.guns has 3026 (95.1%) zerosZeros
tw.healthcare has 2462 (77.4%) zerosZeros
tw.higher.education has 2746 (86.3%) zerosZeros
tw.immigration has 2835 (89.1%) zerosZeros
tw.indian.affairs has 2766 (86.9%) zerosZeros
tw.intelligence.and.surveillance has 2595 (81.6%) zerosZeros
tw.labor has 2777 (87.3%) zerosZeros
tw.law.courts.and.judges has 2139 (67.2%) zerosZeros
tw.transportation has 2632 (82.7%) zerosZeros
tw.veterans.affairs has 2744 (86.2%) zerosZeros
tw.womens.issues has 2731 (85.8%) zerosZeros

Reproduction

Analysis started2023-08-28 21:31:49.640534
Analysis finished2023-08-28 21:33:05.030518
Duration1 minute and 15.39 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

bill.id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct3182
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
113_hr83
 
1
110_hres32
 
1
110_hr3648
 
1
110_hres703
 
1
110_hres704
 
1
Other values (3177)
3177 

Length

Max length18
Median length16
Mean length10.263671
Min length6

Characters and Unicode

Total characters32659
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3182 ?
Unique (%)100.0%

Sample

1st row113_hr83
2nd row113_pn1070
3rd row113_pn1099
4th row113_pn1160
5th row113_pn1297

Common Values

ValueCountFrequency (%)
113_hr83 1
 
< 0.1%
110_hres32 1
 
< 0.1%
110_hr3648 1
 
< 0.1%
110_hres703 1
 
< 0.1%
110_hres704 1
 
< 0.1%
110_hr928 1
 
< 0.1%
110_hres701 1
 
< 0.1%
110_hres702 1
 
< 0.1%
110_hconres200 1
 
< 0.1%
110_hconres203 1
 
< 0.1%
Other values (3172) 3172
99.7%

Length

2023-08-28T14:33:05.261490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
113_hr83 1
 
< 0.1%
113_pn1069 1
 
< 0.1%
113_hres696 1
 
< 0.1%
113_hr5759 1
 
< 0.1%
113_pn1099 1
 
< 0.1%
113_pn1160 1
 
< 0.1%
113_pn1297 1
 
< 0.1%
113_pn1345 1
 
< 0.1%
113_pn1504 1
 
< 0.1%
113_pn1561 1
 
< 0.1%
Other values (3172) 3172
99.7%

Most occurring characters

ValueCountFrequency (%)
1 7859
24.1%
_ 3182
9.7%
r 2574
 
7.9%
h 2500
 
7.7%
0 2300
 
7.0%
2 1862
 
5.7%
3 1802
 
5.5%
s 1614
 
4.9%
e 1299
 
4.0%
8 1246
 
3.8%
Other values (20) 6421
19.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20183
61.8%
Lowercase Letter 9284
28.4%
Connector Punctuation 3182
 
9.7%
Dash Punctuation 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 2574
27.7%
h 2500
26.9%
s 1614
17.4%
e 1299
14.0%
n 489
 
5.3%
p 339
 
3.7%
o 160
 
1.7%
c 134
 
1.4%
j 97
 
1.0%
u 18
 
0.2%
Other values (8) 60
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 7859
38.9%
0 2300
 
11.4%
2 1862
 
9.2%
3 1802
 
8.9%
8 1246
 
6.2%
9 1155
 
5.7%
4 1067
 
5.3%
5 1040
 
5.2%
6 959
 
4.8%
7 893
 
4.4%
Connector Punctuation
ValueCountFrequency (%)
_ 3182
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23375
71.6%
Latin 9284
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 2574
27.7%
h 2500
26.9%
s 1614
17.4%
e 1299
14.0%
n 489
 
5.3%
p 339
 
3.7%
o 160
 
1.7%
c 134
 
1.4%
j 97
 
1.0%
u 18
 
0.2%
Other values (8) 60
 
0.6%
Common
ValueCountFrequency (%)
1 7859
33.6%
_ 3182
13.6%
0 2300
 
9.8%
2 1862
 
8.0%
3 1802
 
7.7%
8 1246
 
5.3%
9 1155
 
4.9%
4 1067
 
4.6%
5 1040
 
4.4%
6 959
 
4.1%
Other values (2) 903
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7859
24.1%
_ 3182
9.7%
r 2574
 
7.9%
h 2500
 
7.7%
0 2300
 
7.0%
2 1862
 
5.7%
3 1802
 
5.5%
s 1614
 
4.9%
e 1299
 
4.0%
8 1246
 
3.8%
Other values (20) 6421
19.7%

year
Real number (ℝ)

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.8906
Minimum2003
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:05.351951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2003
Q12007
median2009
Q32012
95-th percentile2014
Maximum2014
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3221623
Coefficient of variation (CV)0.0016537298
Kurtosis-1.0216189
Mean2008.8906
Median Absolute Deviation (MAD)2
Skewness-0.068334014
Sum6392290
Variance11.036762
MonotonicityDecreasing
2023-08-28T14:33:05.435734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2007 427
13.4%
2009 360
11.3%
2014 355
11.2%
2008 316
9.9%
2013 267
8.4%
2011 257
8.1%
2010 236
7.4%
2012 220
6.9%
2003 216
6.8%
2005 205
6.4%
Other values (2) 323
10.2%
ValueCountFrequency (%)
2003 216
6.8%
2004 173
5.4%
2005 205
6.4%
2006 150
 
4.7%
2007 427
13.4%
2008 316
9.9%
2009 360
11.3%
2010 236
7.4%
2011 257
8.1%
2012 220
6.9%
ValueCountFrequency (%)
2014 355
11.2%
2013 267
8.4%
2012 220
6.9%
2011 257
8.1%
2010 236
7.4%
2009 360
11.3%
2008 316
9.9%
2007 427
13.4%
2006 150
 
4.7%
2005 205
6.4%

date
Categorical

Distinct1291
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
2014-12-13
 
14
2010-09-29
 
13
2003-07-09
 
11
2008-09-26
 
10
2014-03-05
 
10
Other values (1286)
3124 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters31820
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique432 ?
Unique (%)13.6%

Sample

1st row2014-12-13
2nd row2014-12-13
3rd row2014-12-13
4th row2014-12-13
5th row2014-12-13

Common Values

ValueCountFrequency (%)
2014-12-13 14
 
0.4%
2010-09-29 13
 
0.4%
2003-07-09 11
 
0.3%
2008-09-26 10
 
0.3%
2014-03-05 10
 
0.3%
2007-07-31 10
 
0.3%
2007-11-14 10
 
0.3%
2007-07-11 9
 
0.3%
2014-12-04 9
 
0.3%
2014-12-03 9
 
0.3%
Other values (1281) 3077
96.7%

Length

2023-08-28T14:33:05.517931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-12-13 14
 
0.4%
2010-09-29 13
 
0.4%
2003-07-09 11
 
0.3%
2008-09-26 10
 
0.3%
2014-03-05 10
 
0.3%
2007-07-31 10
 
0.3%
2007-11-14 10
 
0.3%
2007-07-11 9
 
0.3%
2014-12-04 9
 
0.3%
2014-12-03 9
 
0.3%
Other values (1281) 3077
96.7%

Most occurring characters

ValueCountFrequency (%)
0 9195
28.9%
- 6364
20.0%
2 5155
16.2%
1 3984
12.5%
3 1285
 
4.0%
7 1188
 
3.7%
4 1116
 
3.5%
9 1009
 
3.2%
5 925
 
2.9%
6 908
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25456
80.0%
Dash Punctuation 6364
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9195
36.1%
2 5155
20.3%
1 3984
15.7%
3 1285
 
5.0%
7 1188
 
4.7%
4 1116
 
4.4%
9 1009
 
4.0%
5 925
 
3.6%
6 908
 
3.6%
8 691
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 6364
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9195
28.9%
- 6364
20.0%
2 5155
16.2%
1 3984
12.5%
3 1285
 
4.0%
7 1188
 
3.7%
4 1116
 
3.5%
9 1009
 
3.2%
5 925
 
2.9%
6 908
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9195
28.9%
- 6364
20.0%
2 5155
16.2%
1 3984
12.5%
3 1285
 
4.0%
7 1188
 
3.7%
4 1116
 
3.5%
9 1009
 
3.2%
5 925
 
2.9%
6 908
 
2.9%

bill.str
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3156
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size25.0 KiB
JOURNAL|On Approving the Journal|
 
6
ADJOURN|On Motion to Adjourn|
 
6
H R 10|On Agreeing to the Amendment|
 
2
H R 2641|On Agreeing to the Amendment|
 
2
MOTION|Table Appeal of the Ruling of the Chair|
 
2
Other values (3151)
3164 

Length

Max length457
Median length316
Mean length146.25204
Min length22

Characters and Unicode

Total characters465374
Distinct characters79
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3138 ?
Unique (%)98.6%

Sample

1st rowH.R. 83|On the Motion to Proceed H.R. 83|To require the Secretary of the Interior to assemble a team of technical, policy, and financial experts to address the energy needs of the insular areas of the United States and the Freely Associated States through the development of energy action plans aimed at promoting access to affordable, reliable energy, including increasing use of indigenous clean-energy resources, and for other purposes.
2nd rowPN1070|On the Motion to Proceed PN1070|Christopher Smith, of Texas, to be an Assistant Secretary of Energy (Fossil Energy)
3rd rowPN1099|On the Motion to Proceed PN1099|Frank A. Rose, of Massachusetts, to be an Assistant Secretary of State (Verification and Compliance)
4th rowPN1160|On the Motion to Proceed PN1160|Vivek Hallegere Murthy, of Massachusetts, to be Medical Director in the Regular Corps of the Public Health Service, subject to qualifications therefor as provided by law and regulations, and to be Surgeon General of the Public Health Service for a term of four years
5th rowPN1297|On the Motion to Proceed PN1297|John Charles Cruden, of Virginia, to be an Assistant Attorney General

Common Values

ValueCountFrequency (%)
JOURNAL|On Approving the Journal| 6
 
0.2%
ADJOURN|On Motion to Adjourn| 6
 
0.2%
H R 10|On Agreeing to the Amendment| 2
 
0.1%
H R 2641|On Agreeing to the Amendment| 2
 
0.1%
MOTION|Table Appeal of the Ruling of the Chair| 2
 
0.1%
H R 5522|On Agreeing to the Amendment| 2
 
0.1%
H R 5|On Agreeing to the Amendment| 2
 
0.1%
H R 2218|On Agreeing to the Amendment| 2
 
0.1%
H R 3|On Agreeing to the Amendment| 2
 
0.1%
H R 3010|On Agreeing to the Amendment| 2
 
0.1%
Other values (3146) 3154
99.1%

Length

2023-08-28T14:33:05.623369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 6750
 
9.3%
to 4149
 
5.7%
of 3877
 
5.3%
and 2886
 
4.0%
h 2521
 
3.5%
for 2223
 
3.1%
motion 1571
 
2.2%
res 1316
 
1.8%
r 1262
 
1.7%
act 1126
 
1.5%
Other values (9041) 44996
61.9%

Most occurring characters

ValueCountFrequency (%)
69499
14.9%
e 40305
 
8.7%
t 32963
 
7.1%
o 32851
 
7.1%
n 31999
 
6.9%
i 25593
 
5.5%
r 22297
 
4.8%
a 20201
 
4.3%
s 18380
 
3.9%
d 13093
 
2.8%
Other values (69) 158193
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 317761
68.3%
Space Separator 69499
 
14.9%
Uppercase Letter 43203
 
9.3%
Decimal Number 20123
 
4.3%
Other Punctuation 7166
 
1.5%
Math Symbol 6364
 
1.4%
Close Punctuation 371
 
0.1%
Open Punctuation 371
 
0.1%
Dash Punctuation 273
 
0.1%
Final Punctuation 171
 
< 0.1%
Other values (2) 72
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 40305
12.7%
t 32963
10.4%
o 32851
10.3%
n 31999
10.1%
i 25593
 
8.1%
r 22297
 
7.0%
a 20201
 
6.4%
s 18380
 
5.8%
d 13093
 
4.1%
h 10335
 
3.3%
Other values (16) 69744
21.9%
Uppercase Letter
ValueCountFrequency (%)
R 5990
13.9%
A 4923
11.4%
S 4707
10.9%
O 4061
9.4%
H 3957
9.2%
P 3399
7.9%
M 2553
 
5.9%
C 2477
 
5.7%
E 1893
 
4.4%
N 1463
 
3.4%
Other values (16) 7780
18.0%
Decimal Number
ValueCountFrequency (%)
2 3112
15.5%
1 3109
15.4%
0 2811
14.0%
3 1996
9.9%
4 1713
8.5%
5 1628
8.1%
6 1504
7.5%
8 1429
7.1%
9 1412
7.0%
7 1409
7.0%
Other Punctuation
ValueCountFrequency (%)
. 3573
49.9%
, 3209
44.8%
; 336
 
4.7%
: 20
 
0.3%
/ 11
 
0.2%
" 8
 
0.1%
' 5
 
0.1%
& 4
 
0.1%
Final Punctuation
ValueCountFrequency (%)
109
63.7%
62
36.3%
Space Separator
ValueCountFrequency (%)
69499
100.0%
Math Symbol
ValueCountFrequency (%)
| 6364
100.0%
Close Punctuation
ValueCountFrequency (%)
) 371
100.0%
Open Punctuation
ValueCountFrequency (%)
( 371
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 273
100.0%
Initial Punctuation
ValueCountFrequency (%)
64
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 360964
77.6%
Common 104410
 
22.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 40305
 
11.2%
t 32963
 
9.1%
o 32851
 
9.1%
n 31999
 
8.9%
i 25593
 
7.1%
r 22297
 
6.2%
a 20201
 
5.6%
s 18380
 
5.1%
d 13093
 
3.6%
h 10335
 
2.9%
Other values (42) 112947
31.3%
Common
ValueCountFrequency (%)
69499
66.6%
| 6364
 
6.1%
. 3573
 
3.4%
, 3209
 
3.1%
2 3112
 
3.0%
1 3109
 
3.0%
0 2811
 
2.7%
3 1996
 
1.9%
4 1713
 
1.6%
5 1628
 
1.6%
Other values (17) 7396
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 465139
99.9%
Punctuation 235
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
69499
14.9%
e 40305
 
8.7%
t 32963
 
7.1%
o 32851
 
7.1%
n 31999
 
6.9%
i 25593
 
5.5%
r 22297
 
4.8%
a 20201
 
4.3%
s 18380
 
4.0%
d 13093
 
2.8%
Other values (66) 157958
34.0%
Punctuation
ValueCountFrequency (%)
109
46.4%
64
27.2%
62
26.4%

bill.desc
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct2691
Distinct (%)96.6%
Missing397
Missing (%)12.5%
Memory size25.0 KiB
Waiving a requirement of clause 6(a) of rule XIII with respect to consideration of certain resolutions reported from the Committee on Rules
 
12
Providing for an adjournment or recess of the two Houses
 
11
Providing for consideration of motions to suspend the rules
 
9
Resolution Raising a Question of the Privileges of the House
 
7
Raising a question of the privileges of the House
 
6
Other values (2686)
2740 

Length

Max length420
Median length252
Mean length116.91059
Min length10

Characters and Unicode

Total characters325596
Distinct characters78
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2638 ?
Unique (%)94.7%

Sample

1st rowTo require the Secretary of the Interior to assemble a team of technical, policy, and financial experts to address the energy needs of the insular areas of the United States and the Freely Associated States through the development of energy action plans aimed at promoting access to affordable, reliable energy, including increasing use of indigenous clean-energy resources, and for other purposes.
2nd rowChristopher Smith, of Texas, to be an Assistant Secretary of Energy (Fossil Energy)
3rd rowFrank A. Rose, of Massachusetts, to be an Assistant Secretary of State (Verification and Compliance)
4th rowVivek Hallegere Murthy, of Massachusetts, to be Medical Director in the Regular Corps of the Public Health Service, subject to qualifications therefor as provided by law and regulations, and to be Surgeon General of the Public Health Service for a term of four years
5th rowJohn Charles Cruden, of Virginia, to be an Assistant Attorney General

Common Values

ValueCountFrequency (%)
Waiving a requirement of clause 6(a) of rule XIII with respect to consideration of certain resolutions reported from the Committee on Rules 12
 
0.4%
Providing for an adjournment or recess of the two Houses 11
 
0.3%
Providing for consideration of motions to suspend the rules 9
 
0.3%
Resolution Raising a Question of the Privileges of the House 7
 
0.2%
Raising a question of the privileges of the House 6
 
0.2%
Adjournment Resolution 5
 
0.2%
Expressing the sense of the House of Representatives regarding the terrorist attacks launched against the United States on September 11, 2001 3
 
0.1%
Supporting the goals and ideals of National Train Day 3
 
0.1%
Sherman of California Amendment 3
 
0.1%
To provide for the establishment of the Special Envoy to Promote Religious Freedom of Religious Minorities in the Near East and South Central Asia 2
 
0.1%
Other values (2681) 2724
85.6%
(Missing) 397
 
12.5%

Length

2023-08-28T14:33:05.752712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 3809
 
7.6%
of 3750
 
7.5%
to 2411
 
4.8%
for 2223
 
4.4%
and 1881
 
3.7%
act 1125
 
2.2%
consideration 750
 
1.5%
providing 745
 
1.5%
h.r 677
 
1.3%
a 471
 
0.9%
Other values (5930) 32462
64.5%

Most occurring characters

ValueCountFrequency (%)
47520
14.6%
e 28415
 
8.7%
o 24776
 
7.6%
t 23886
 
7.3%
n 20953
 
6.4%
i 20611
 
6.3%
r 19143
 
5.9%
a 17452
 
5.4%
s 12711
 
3.9%
d 8940
 
2.7%
Other values (68) 101189
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 241351
74.1%
Space Separator 47520
 
14.6%
Uppercase Letter 21063
 
6.5%
Decimal Number 8392
 
2.6%
Other Punctuation 6029
 
1.9%
Close Punctuation 368
 
0.1%
Open Punctuation 368
 
0.1%
Dash Punctuation 262
 
0.1%
Final Punctuation 171
 
0.1%
Initial Punctuation 64
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28415
11.8%
o 24776
10.3%
t 23886
9.9%
n 20953
 
8.7%
i 20611
 
8.5%
r 19143
 
7.9%
a 17452
 
7.2%
s 12711
 
5.3%
d 8940
 
3.7%
c 8498
 
3.5%
Other values (16) 55966
23.2%
Uppercase Letter
ValueCountFrequency (%)
A 2739
13.0%
S 2017
 
9.6%
C 1945
 
9.2%
R 1894
 
9.0%
P 1573
 
7.5%
H 1441
 
6.8%
D 973
 
4.6%
M 958
 
4.5%
T 953
 
4.5%
I 833
 
4.0%
Other values (16) 5737
27.2%
Decimal Number
ValueCountFrequency (%)
0 1910
22.8%
2 1568
18.7%
1 1210
14.4%
3 710
 
8.5%
4 552
 
6.6%
9 531
 
6.3%
5 506
 
6.0%
8 491
 
5.9%
6 473
 
5.6%
7 441
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 2912
48.3%
, 2734
45.3%
; 336
 
5.6%
: 19
 
0.3%
/ 11
 
0.2%
" 8
 
0.1%
' 5
 
0.1%
& 4
 
0.1%
Final Punctuation
ValueCountFrequency (%)
109
63.7%
62
36.3%
Space Separator
ValueCountFrequency (%)
47520
100.0%
Close Punctuation
ValueCountFrequency (%)
) 368
100.0%
Open Punctuation
ValueCountFrequency (%)
( 368
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 262
100.0%
Initial Punctuation
ValueCountFrequency (%)
64
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 262414
80.6%
Common 63182
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28415
 
10.8%
o 24776
 
9.4%
t 23886
 
9.1%
n 20953
 
8.0%
i 20611
 
7.9%
r 19143
 
7.3%
a 17452
 
6.7%
s 12711
 
4.8%
d 8940
 
3.4%
c 8498
 
3.2%
Other values (42) 77029
29.4%
Common
ValueCountFrequency (%)
47520
75.2%
. 2912
 
4.6%
, 2734
 
4.3%
0 1910
 
3.0%
2 1568
 
2.5%
1 1210
 
1.9%
3 710
 
1.1%
4 552
 
0.9%
9 531
 
0.8%
5 506
 
0.8%
Other values (16) 3029
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 325361
99.9%
Punctuation 235
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47520
14.6%
e 28415
 
8.7%
o 24776
 
7.6%
t 23886
 
7.3%
n 20953
 
6.4%
i 20611
 
6.3%
r 19143
 
5.9%
a 17452
 
5.4%
s 12711
 
3.9%
d 8940
 
2.7%
Other values (65) 100954
31.0%
Punctuation
ValueCountFrequency (%)
109
46.4%
64
27.2%
62
26.4%

congno
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.71622
Minimum108
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:05.840796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile108
Q1110
median111
Q3112
95-th percentile113
Maximum113
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.621215
Coefficient of variation (CV)0.014642977
Kurtosis-1.0618958
Mean110.71622
Median Absolute Deviation (MAD)1
Skewness-0.1085445
Sum352299
Variance2.628338
MonotonicityDecreasing
2023-08-28T14:33:05.916868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
110 743
23.4%
113 618
19.4%
111 596
18.7%
112 481
15.1%
108 389
12.2%
109 355
11.2%
ValueCountFrequency (%)
108 389
12.2%
109 355
11.2%
110 743
23.4%
111 596
18.7%
112 481
15.1%
113 618
19.4%
ValueCountFrequency (%)
113 618
19.4%
112 481
15.1%
111 596
18.7%
110 743
23.4%
109 355
11.2%
108 389
12.2%

sponsors
Categorical

HIGH CARDINALITY  MISSING 

Distinct616
Distinct (%)22.3%
Missing416
Missing (%)13.1%
Memory size25.0 KiB
cand1484
 
64
cand756
 
46
cand1417
 
45
cand1455
 
44
cand923
 
41
Other values (611)
2526 

Length

Max length10
Median length7
Mean length7.5159074
Min length6

Characters and Unicode

Total characters20789
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201 ?
Unique (%)7.3%

Sample

1st rowcand43257
2nd rowcand1597
3rd rowcand831
4th rowcand43271
5th rowcand1284

Common Values

ValueCountFrequency (%)
cand1484 64
 
2.0%
cand756 46
 
1.4%
cand1417 45
 
1.4%
cand1455 44
 
1.4%
cand923 41
 
1.3%
cand499 35
 
1.1%
cand1276 33
 
1.0%
cand910 32
 
1.0%
cand944 31
 
1.0%
cand157 30
 
0.9%
Other values (606) 2365
74.3%
(Missing) 416
 
13.1%

Length

2023-08-28T14:33:06.011830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cand1484 64
 
2.3%
cand756 46
 
1.7%
cand1417 45
 
1.6%
cand1455 44
 
1.6%
cand923 41
 
1.5%
cand499 35
 
1.3%
cand1276 33
 
1.2%
cand910 32
 
1.2%
cand944 31
 
1.1%
cand157 30
 
1.1%
Other values (606) 2365
85.5%

Most occurring characters

ValueCountFrequency (%)
c 2766
13.3%
a 2766
13.3%
n 2766
13.3%
d 2766
13.3%
1 2167
10.4%
4 1044
 
5.0%
5 989
 
4.8%
2 931
 
4.5%
0 860
 
4.1%
7 836
 
4.0%
Other values (4) 2898
13.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11064
53.2%
Decimal Number 9725
46.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2167
22.3%
4 1044
10.7%
5 989
10.2%
2 931
9.6%
0 860
 
8.8%
7 836
 
8.6%
8 766
 
7.9%
9 748
 
7.7%
3 702
 
7.2%
6 682
 
7.0%
Lowercase Letter
ValueCountFrequency (%)
c 2766
25.0%
a 2766
25.0%
n 2766
25.0%
d 2766
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11064
53.2%
Common 9725
46.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2167
22.3%
4 1044
10.7%
5 989
10.2%
2 931
9.6%
0 860
 
8.8%
7 836
 
8.6%
8 766
 
7.9%
9 748
 
7.7%
3 702
 
7.2%
6 682
 
7.0%
Latin
ValueCountFrequency (%)
c 2766
25.0%
a 2766
25.0%
n 2766
25.0%
d 2766
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20789
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 2766
13.3%
a 2766
13.3%
n 2766
13.3%
d 2766
13.3%
1 2167
10.4%
4 1044
 
5.0%
5 989
 
4.8%
2 931
 
4.5%
0 860
 
4.1%
7 836
 
4.0%
Other values (4) 2898
13.9%

cosponsors
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct1593
Distinct (%)96.7%
Missing1535
Missing (%)48.2%
Memory size25.0 KiB
cand157
 
5
cand1225
 
5
cand1493
 
4
cand362
 
4
cand1254|cand832|cand1516|cand1356|cand1301|cand1225|cand851|cand1477|cand858|cand1231|cand515|cand303|cand877|cand899|cand913|cand919|cand928|cand937|cand943|cand944|cand955|cand957
 
3
Other values (1588)
1626 

Length

Max length3389
Median length1233
Mean length328.32969
Min length6

Characters and Unicode

Total characters540759
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1552 ?
Unique (%)94.2%

Sample

1st rowcand1307|cand953|cand1128
2nd rowcand105168
3rd rowcand1240|cand210|cand1505|cand1503|cand1251|cand823|cand1341|cand1252|cand1499|cand1433|cand1254|cand824|cand1256|cand1507|cand1195|cand1260|cand1262|cand1435|cand828|cand1512|cand1198|cand830|cand1267|cand1270|cand163|cand806|cand1276|cand1278|cand744|cand1514|cand1285|cand1287|cand1443|cand1515|cand1444|cand707|cand1359|cand1362|cand541|cand1424|cand788|cand1455|cand1456|cand346|cand1523|cand839|cand758|cand53|cand842|cand1215|cand867|cand844|cand1525|cand1220|cand1376|cand550|cand766|cand1467|cand1378|cand1527|cand849|cand1314|cand1470|cand1501|cand1529|cand1316|cand1317|cand1318|cand157|cand1222|cand776|cand784|cand756|cand1383|cand750|cand677|cand1472|cand1387|cand615|cand850|cand1536|cand1425|cand723|cand1396|cand1398|cand856|cand858|cand767|cand1231|cand515|cand1484|cand670|cand859|cand1485|cand1408|cand1487|cand1409|cand1488|cand1410|cand757|cand1555|cand1556|cand860|cand863|cand1559|cand1237|cand556|cand865|cand1417|cand785|cand866|cand359|cand1494|cand447|cand868|cand869|cand872|cand875|cand876|cand882|cand885|cand887|cand894|cand895|cand896|cand897|cand899|cand903|cand904|cand906|cand908|cand909|cand910|cand911|cand912|cand605|cand917|cand918|cand919|cand921|cand922|cand923|cand121877|cand924|cand927|cand928|cand929|cand931|cand940|cand941|cand942|cand945|cand947|cand948|cand949|cand951|cand952|cand953|cand954|cand955|cand956|cand958|cand959|cand960|cand967|cand1026|cand970|cand971|cand1022|cand973|cand974|cand977|cand1017|cand980|cand1021|cand984|cand985|cand987|cand1023|cand988|cand1027|cand1018|cand989|cand990|cand991|cand992|cand995|cand964|cand997|cand1002|cand1019|cand1068|cand1012|cand1014|cand1020|cand1029|cand134698|cand1031|cand1033|cand1034|cand1035|cand1036|cand1037|cand1041|cand1042|cand1043|cand1045|cand1046|cand1047|cand1050|cand1051|cand1052|cand1053|cand1054|cand1056|cand1059|cand1061|cand1062|cand1071|cand1073|cand1074|cand1075|cand1079|cand1081|cand1082|cand1084|cand1085|cand1086|cand1090|cand1091|cand1092|cand1093|cand1094|cand1096|cand1097|cand1098|cand1099|cand1100|cand1101|cand1102|cand1103|cand1104|cand1105|cand1106|cand1107|cand1108|cand1109|cand1114|cand1119|cand1120|cand1122|cand1123|cand1124|cand1126|cand1127|cand1128|cand1130|cand1131|cand1132|cand1133|cand1137|cand1138|cand1139|cand1140|cand1141|cand1143|cand1144|cand1146|cand1147|cand1149|cand1151|cand1152|cand1153|cand1154|cand1156|cand1157|cand1158|cand1159|cand1160|cand1164|cand1165|cand1166|cand1167|cand1168|cand1169|cand1170|cand1172|cand1173|cand1174|cand1175|cand1176|cand1177|cand1178|cand1179|cand1180|cand1183|cand1184|cand44313|cand40344|cand42266|cand39934|cand1338|cand40348|cand189537|cand40394|cand40450|cand35060|cand40481|cand40569|cand40621|cand40368|cand40734|cand40829|cand40871|cand40981|cand40366|cand41075|cand41113|cand41261|cand41398|cand41326|cand41546|cand41493|cand41479|cand54165|cand41667|cand41775|cand50384|cand41950|cand119903|cand119904|cand42011|cand36557|cand42104|cand36550|cand36826|cand42611|cand42392|cand42732|cand42792|cand280|cand42972|cand104809|cand43733|cand37909|cand43557|cand55619|cand43796|cand43832|cand43941|cand43257|cand43271|cand43291|cand43321|cand78036|cand44045|cand44057|cand121656|cand38921|cand44515|cand44665|cand44995|cand45198|cand45059|cand45174|cand45177|cand45127|cand1407|cand45500|cand39888|cand45555
4th rowcand823|cand1314|cand1318|cand1331|cand887|cand894|cand924|cand929|cand947|cand1013|cand1050|cand1053|cand1100|cand1108|cand1141|cand1152|cand1184|cand189537|cand40450|cand40481|cand40569|cand40829|cand40871|cand40366|cand41075|cand41546|cand38921|cand45177|cand45500
5th rowcand1590

Common Values

ValueCountFrequency (%)
cand157 5
 
0.2%
cand1225 5
 
0.2%
cand1493 4
 
0.1%
cand362 4
 
0.1%
cand1254|cand832|cand1516|cand1356|cand1301|cand1225|cand851|cand1477|cand858|cand1231|cand515|cand303|cand877|cand899|cand913|cand919|cand928|cand937|cand943|cand944|cand955|cand957 3
 
0.1%
cand1638 3
 
0.1%
cand522 3
 
0.1%
cand310 2
 
0.1%
cand511 2
 
0.1%
cand757 2
 
0.1%
Other values (1583) 1614
50.7%
(Missing) 1535
48.2%

Length

2023-08-28T14:33:06.138277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cand157 5
 
0.3%
cand1225 5
 
0.3%
cand1493 4
 
0.2%
cand362 4
 
0.2%
cand1254|cand832|cand1516|cand1356|cand1301|cand1225|cand851|cand1477|cand858|cand1231|cand515|cand303|cand877|cand899|cand913|cand919|cand928|cand937|cand943|cand944|cand955|cand957 3
 
0.2%
cand1638 3
 
0.2%
cand522 3
 
0.2%
cand1514 2
 
0.1%
cand1318 2
 
0.1%
cand1301 2
 
0.1%
Other values (1583) 1614
98.0%

Most occurring characters

ValueCountFrequency (%)
c 63169
11.7%
a 63169
11.7%
n 63169
11.7%
d 63169
11.7%
| 61522
11.4%
1 51283
9.5%
5 22790
 
4.2%
9 22598
 
4.2%
4 21800
 
4.0%
8 21022
 
3.9%
Other values (5) 87068
16.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 252676
46.7%
Decimal Number 226561
41.9%
Math Symbol 61522
 
11.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 51283
22.6%
5 22790
10.1%
9 22598
10.0%
4 21800
9.6%
8 21022
9.3%
2 18752
 
8.3%
0 17924
 
7.9%
3 17456
 
7.7%
7 17407
 
7.7%
6 15529
 
6.9%
Lowercase Letter
ValueCountFrequency (%)
c 63169
25.0%
a 63169
25.0%
n 63169
25.0%
d 63169
25.0%
Math Symbol
ValueCountFrequency (%)
| 61522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 288083
53.3%
Latin 252676
46.7%

Most frequent character per script

Common
ValueCountFrequency (%)
| 61522
21.4%
1 51283
17.8%
5 22790
 
7.9%
9 22598
 
7.8%
4 21800
 
7.6%
8 21022
 
7.3%
2 18752
 
6.5%
0 17924
 
6.2%
3 17456
 
6.1%
7 17407
 
6.0%
Latin
ValueCountFrequency (%)
c 63169
25.0%
a 63169
25.0%
n 63169
25.0%
d 63169
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 540759
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 63169
11.7%
a 63169
11.7%
n 63169
11.7%
d 63169
11.7%
| 61522
11.4%
1 51283
9.5%
5 22790
 
4.2%
9 22598
 
4.2%
4 21800
 
4.0%
8 21022
 
3.9%
Other values (5) 87068
16.1%

tw.latent1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2197
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65416071
Minimum0
Maximum1
Zeros191
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:06.260404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4805421
median0.67534823
Q30.99999645
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.51945436

Descriptive statistics

Standard deviation0.3059559
Coefficient of variation (CV)0.46770755
Kurtosis-0.52024789
Mean0.65416071
Median Absolute Deviation (MAD)0.2463982
Skewness-0.61558562
Sum2081.5394
Variance0.093609014
MonotonicityNot monotonic
2023-08-28T14:33:06.374916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 796
 
25.0%
0 191
 
6.0%
0.2174302145 1
 
< 0.1%
0.6402668283 1
 
< 0.1%
0.5749256359 1
 
< 0.1%
0.6696440045 1
 
< 0.1%
0.4220590592 1
 
< 0.1%
0.2307107623 1
 
< 0.1%
0.499530727 1
 
< 0.1%
0.8523252288 1
 
< 0.1%
Other values (2187) 2187
68.7%
ValueCountFrequency (%)
0 191
6.0%
3.587316459 × 10-61
 
< 0.1%
4.823569025 × 10-61
 
< 0.1%
7.648792317 × 10-61
 
< 0.1%
1.697437836 × 10-51
 
< 0.1%
1.756912665 × 10-51
 
< 0.1%
2.223429349 × 10-51
 
< 0.1%
2.30252409 × 10-51
 
< 0.1%
2.317281846 × 10-51
 
< 0.1%
2.544585638 × 10-51
 
< 0.1%
ValueCountFrequency (%)
1 796
25.0%
0.999985818 1
 
< 0.1%
0.9999795174 1
 
< 0.1%
0.9999749911 1
 
< 0.1%
0.9999453418 1
 
< 0.1%
0.9999392364 1
 
< 0.1%
0.9999357724 1
 
< 0.1%
0.9999011313 1
 
< 0.1%
0.9997188804 1
 
< 0.1%
0.9995833691 1
 
< 0.1%

tw.abortion.and.social.conservatism
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct567
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0077853463
Minimum0
Maximum0.56949397
Zeros2616
Zeros (%)82.2%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:06.487142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.033767647
Maximum0.56949397
Range0.56949397
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.039334158
Coefficient of variation (CV)5.0523324
Kurtosis78.142953
Mean0.0077853463
Median Absolute Deviation (MAD)0
Skewness8.2512675
Sum24.772972
Variance0.001547176
MonotonicityNot monotonic
2023-08-28T14:33:06.603325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2616
82.2%
0.003922747228 1
 
< 0.1%
0.001145264462 1
 
< 0.1%
0.01041278013 1
 
< 0.1%
0.03504604066 1
 
< 0.1%
6.590096352 × 10-51
 
< 0.1%
0.004230310542 1
 
< 0.1%
0.06029546261 1
 
< 0.1%
0.01676877755 1
 
< 0.1%
0.006697511503 1
 
< 0.1%
Other values (557) 557
 
17.5%
ValueCountFrequency (%)
0 2616
82.2%
8.682574291 × 10-61
 
< 0.1%
9.052462547 × 10-61
 
< 0.1%
9.319039477 × 10-61
 
< 0.1%
9.570875143 × 10-61
 
< 0.1%
1.006028604 × 10-51
 
< 0.1%
1.226882392 × 10-51
 
< 0.1%
1.335709686 × 10-51
 
< 0.1%
1.475865384 × 10-51
 
< 0.1%
1.581465981 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.5694939684 1
< 0.1%
0.4849439402 1
< 0.1%
0.4624956856 1
< 0.1%
0.4559103813 1
< 0.1%
0.4493265472 1
< 0.1%
0.4202375112 1
< 0.1%
0.4126196538 1
< 0.1%
0.4077861671 1
< 0.1%
0.4077573586 1
< 0.1%
0.4022928341 1
< 0.1%

tw.agriculture
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct482
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0032739552
Minimum0
Maximum0.35264451
Zeros2701
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:06.721168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0081440744
Maximum0.35264451
Range0.35264451
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.022771745
Coefficient of variation (CV)6.9554236
Kurtosis118.44375
Mean0.0032739552
Median Absolute Deviation (MAD)0
Skewness10.296737
Sum10.417725
Variance0.00051855237
MonotonicityNot monotonic
2023-08-28T14:33:06.832560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2701
84.9%
0.2184865199 1
 
< 0.1%
0.0003499762023 1
 
< 0.1%
0.00306904383 1
 
< 0.1%
0.02746648989 1
 
< 0.1%
0.2458727007 1
 
< 0.1%
3.861495135 × 10-51
 
< 0.1%
0.1677468507 1
 
< 0.1%
0.004950043115 1
 
< 0.1%
0.0009928701744 1
 
< 0.1%
Other values (472) 472
 
14.8%
ValueCountFrequency (%)
0 2701
84.9%
4.417218782 × 10-61
 
< 0.1%
5.893965925 × 10-61
 
< 0.1%
7.408909745 × 10-61
 
< 0.1%
8.667341816 × 10-61
 
< 0.1%
8.796491582 × 10-61
 
< 0.1%
8.862851385 × 10-61
 
< 0.1%
1.016225082 × 10-51
 
< 0.1%
1.082005331 × 10-51
 
< 0.1%
1.107117575 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.3526445113 1
< 0.1%
0.3429130771 1
< 0.1%
0.3304618555 1
< 0.1%
0.3097256623 1
< 0.1%
0.3020329596 1
< 0.1%
0.3006059187 1
< 0.1%
0.2792407087 1
< 0.1%
0.2582209944 1
< 0.1%
0.2458727007 1
< 0.1%
0.2398314469 1
< 0.1%

tw.banking.and.finance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct806
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010925368
Minimum0
Maximum0.75780741
Zeros2377
Zeros (%)74.7%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:06.954661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38.7338993 × 10-6
95-th percentile0.049332642
Maximum0.75780741
Range0.75780741
Interquartile range (IQR)8.7338993 × 10-6

Descriptive statistics

Standard deviation0.046214354
Coefficient of variation (CV)4.2300045
Kurtosis58.977368
Mean0.010925368
Median Absolute Deviation (MAD)0
Skewness6.7819341
Sum34.76452
Variance0.0021357665
MonotonicityNot monotonic
2023-08-28T14:33:07.065985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2377
74.7%
0.06545463848 1
 
< 0.1%
1.100543058 × 10-51
 
< 0.1%
0.01915565766 1
 
< 0.1%
0.009041545225 1
 
< 0.1%
0.0838345718 1
 
< 0.1%
4.772215145 × 10-51
 
< 0.1%
3.679793913 × 10-51
 
< 0.1%
6.899368874 × 10-51
 
< 0.1%
0.0001183840087 1
 
< 0.1%
Other values (796) 796
 
25.0%
ValueCountFrequency (%)
0 2377
74.7%
5.360861624 × 10-61
 
< 0.1%
5.666897718 × 10-61
 
< 0.1%
5.941818971 × 10-61
 
< 0.1%
6.068737969 × 10-61
 
< 0.1%
6.55276559 × 10-61
 
< 0.1%
6.779484109 × 10-61
 
< 0.1%
7.816268151 × 10-61
 
< 0.1%
7.948202429 × 10-61
 
< 0.1%
8.387993871 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.7578074139 1
< 0.1%
0.5945226657 1
< 0.1%
0.4568530932 1
< 0.1%
0.446255901 1
< 0.1%
0.4014996894 1
< 0.1%
0.3966321542 1
< 0.1%
0.3891956158 1
< 0.1%
0.3887643301 1
< 0.1%
0.3755602254 1
< 0.1%
0.3753755811 1
< 0.1%

tw.civil.rights
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct783
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.022027013
Minimum0
Maximum0.87098302
Zeros2400
Zeros (%)75.4%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:07.197275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0748829
Maximum0.87098302
Range0.87098302
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09535174
Coefficient of variation (CV)4.3288547
Kurtosis33.884674
Mean0.022027013
Median Absolute Deviation (MAD)0
Skewness5.6911715
Sum70.089955
Variance0.0090919542
MonotonicityNot monotonic
2023-08-28T14:33:07.303180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2400
75.4%
0.007445998913 1
 
< 0.1%
0.008819822517 1
 
< 0.1%
0.005367542528 1
 
< 0.1%
0.0002197800498 1
 
< 0.1%
2.36695149 × 10-51
 
< 0.1%
0.02267644756 1
 
< 0.1%
0.1429242059 1
 
< 0.1%
0.05531530127 1
 
< 0.1%
0.0232590476 1
 
< 0.1%
Other values (773) 773
 
24.3%
ValueCountFrequency (%)
0 2400
75.4%
3.344268463 × 10-61
 
< 0.1%
5.547169578 × 10-61
 
< 0.1%
8.457533899 × 10-61
 
< 0.1%
9.116929018 × 10-61
 
< 0.1%
9.62299384 × 10-61
 
< 0.1%
1.024399833 × 10-51
 
< 0.1%
1.086569799 × 10-51
 
< 0.1%
1.162545566 × 10-51
 
< 0.1%
1.241314381 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.8709830222 1
< 0.1%
0.8467580792 1
< 0.1%
0.832792318 1
< 0.1%
0.7921091739 1
< 0.1%
0.7675762092 1
< 0.1%
0.7646163337 1
< 0.1%
0.7638040815 1
< 0.1%
0.758036956 1
< 0.1%
0.7387358819 1
< 0.1%
0.7366217004 1
< 0.1%

tw.congress.and.procedural
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1501
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.033613198
Minimum-3.2039762 × 10-7
Maximum0.99999518
Zeros1682
Zeros (%)52.9%
Negative1
Negative (%)< 0.1%
Memory size25.0 KiB
2023-08-28T14:33:07.422117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-3.2039762 × 10-7
5-th percentile0
Q10
median0
Q30.010529841
95-th percentile0.15691341
Maximum0.99999518
Range0.9999955
Interquartile range (IQR)0.010529841

Descriptive statistics

Standard deviation0.11612891
Coefficient of variation (CV)3.4548606
Kurtosis46.211025
Mean0.033613198
Median Absolute Deviation (MAD)0
Skewness6.3774838
Sum106.9572
Variance0.013485925
MonotonicityNot monotonic
2023-08-28T14:33:07.552249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1682
52.9%
0.000381323061 1
 
< 0.1%
0.06429384443 1
 
< 0.1%
0.03629661273 1
 
< 0.1%
0.09684284596 1
 
< 0.1%
5.342456015 × 10-51
 
< 0.1%
0.9999711544 1
 
< 0.1%
0.01133558095 1
 
< 0.1%
7.801071515 × 10-51
 
< 0.1%
0.05102610877 1
 
< 0.1%
Other values (1491) 1491
46.9%
ValueCountFrequency (%)
-3.20397617 × 10-71
 
< 0.1%
0 1682
52.9%
2.08288239 × 10-61
 
< 0.1%
3.248950713 × 10-61
 
< 0.1%
5.327814207 × 10-61
 
< 0.1%
5.420956946 × 10-61
 
< 0.1%
5.819674619 × 10-61
 
< 0.1%
5.825702222 × 10-61
 
< 0.1%
5.872409399 × 10-61
 
< 0.1%
6.212120147 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.9999951764 1
< 0.1%
0.9999780349 1
< 0.1%
0.9999777657 1
< 0.1%
0.9999768272 1
< 0.1%
0.9999745541 1
< 0.1%
0.9999736653 1
< 0.1%
0.9999735584 1
< 0.1%
0.9999718643 1
< 0.1%
0.9999712928 1
< 0.1%
0.9999711544 1
< 0.1%

tw.crime
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct954
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012576558
Minimum-6.2421667 × 10-7
Maximum0.68223965
Zeros2229
Zeros (%)70.1%
Negative1
Negative (%)< 0.1%
Memory size25.0 KiB
2023-08-28T14:33:07.667011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-6.2421667 × 10-7
5-th percentile0
Q10
median0
Q37.7582183 × 10-5
95-th percentile0.057238702
Maximum0.68223965
Range0.68224027
Interquartile range (IQR)7.7582183 × 10-5

Descriptive statistics

Standard deviation0.048630367
Coefficient of variation (CV)3.8667468
Kurtosis52.926918
Mean0.012576558
Median Absolute Deviation (MAD)0
Skewness6.6127572
Sum40.018609
Variance0.0023649126
MonotonicityNot monotonic
2023-08-28T14:33:07.783230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2229
70.1%
0.01205037702 1
 
< 0.1%
0.01482847785 1
 
< 0.1%
0.002397462326 1
 
< 0.1%
0.08878228339 1
 
< 0.1%
0.01734835527 1
 
< 0.1%
0.05257895927 1
 
< 0.1%
0.00322102831 1
 
< 0.1%
0.01919891986 1
 
< 0.1%
0.01985340688 1
 
< 0.1%
Other values (944) 944
29.7%
ValueCountFrequency (%)
-6.242166745 × 10-71
 
< 0.1%
0 2229
70.1%
3.772680464 × 10-61
 
< 0.1%
5.339748789 × 10-61
 
< 0.1%
7.136999407 × 10-61
 
< 0.1%
8.006030687 × 10-61
 
< 0.1%
8.063707902 × 10-61
 
< 0.1%
8.629420969 × 10-61
 
< 0.1%
9.422018996 × 10-61
 
< 0.1%
1.051823892 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.6822396487 1
< 0.1%
0.586180793 1
< 0.1%
0.5074432555 1
< 0.1%
0.4990229537 1
< 0.1%
0.4774848786 1
< 0.1%
0.4615848153 1
< 0.1%
0.4533309973 1
< 0.1%
0.4284944097 1
< 0.1%
0.4127069837 1
< 0.1%
0.4115571346 1
< 0.1%

tw.defense.and.foreign.policy
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1002
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.033924599
Minimum0
Maximum0.91873552
Zeros2181
Zeros (%)68.5%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:07.901132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0002008387
95-th percentile0.27191593
Maximum0.91873552
Range0.91873552
Interquartile range (IQR)0.0002008387

Descriptive statistics

Standard deviation0.10489132
Coefficient of variation (CV)3.0918958
Kurtosis15.787533
Mean0.033924599
Median Absolute Deviation (MAD)0
Skewness3.8521399
Sum107.94807
Variance0.01100219
MonotonicityNot monotonic
2023-08-28T14:33:08.213289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2181
68.5%
0.02588237503 1
 
< 0.1%
0.002949158118 1
 
< 0.1%
0.2705683804 1
 
< 0.1%
0.00692200715 1
 
< 0.1%
0.05234557025 1
 
< 0.1%
0.1430128431 1
 
< 0.1%
0.02704935671 1
 
< 0.1%
0.4281763407 1
 
< 0.1%
0.1065769904 1
 
< 0.1%
Other values (992) 992
31.2%
ValueCountFrequency (%)
0 2181
68.5%
2.166111018 × 10-61
 
< 0.1%
3.741035726 × 10-61
 
< 0.1%
5.673341998 × 10-61
 
< 0.1%
5.977605224 × 10-61
 
< 0.1%
6.156072546 × 10-61
 
< 0.1%
7.743797443 × 10-61
 
< 0.1%
8.768468915 × 10-61
 
< 0.1%
1.023388545 × 10-51
 
< 0.1%
1.10410886 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.9187355207 1
< 0.1%
0.7903661652 1
< 0.1%
0.7663469015 1
< 0.1%
0.7085741165 1
< 0.1%
0.7012651434 1
< 0.1%
0.6978929739 1
< 0.1%
0.6894679656 1
< 0.1%
0.6753612376 1
< 0.1%
0.6715751291 1
< 0.1%
0.66580364 1
< 0.1%

tw.economy
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1536
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016414049
Minimum-2.1536719 × 10-5
Maximum0.9246613
Zeros1647
Zeros (%)51.8%
Negative2
Negative (%)0.1%
Memory size25.0 KiB
2023-08-28T14:33:08.332493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.1536719 × 10-5
5-th percentile0
Q10
median0
Q30.0032247511
95-th percentile0.096264782
Maximum0.9246613
Range0.92468283
Interquartile range (IQR)0.0032247511

Descriptive statistics

Standard deviation0.050585
Coefficient of variation (CV)3.0818113
Kurtosis60.065316
Mean0.016414049
Median Absolute Deviation (MAD)0
Skewness6.1497079
Sum52.229503
Variance0.0025588422
MonotonicityNot monotonic
2023-08-28T14:33:08.532061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1647
51.8%
0.09001718779 1
 
< 0.1%
0.000394738885 1
 
< 0.1%
8.490868639 × 10-51
 
< 0.1%
6.525699303 × 10-51
 
< 0.1%
0.02650987155 1
 
< 0.1%
0.002814100823 1
 
< 0.1%
0.04782815757 1
 
< 0.1%
3.561943726 × 10-51
 
< 0.1%
1.499821987 × 10-51
 
< 0.1%
Other values (1526) 1526
48.0%
ValueCountFrequency (%)
-2.153671948 × 10-51
 
< 0.1%
-1.508700359 × 10-51
 
< 0.1%
0 1647
51.8%
1.346181386 × 10-71
 
< 0.1%
5.144713933 × 10-71
 
< 0.1%
2.06517729 × 10-61
 
< 0.1%
3.212623396 × 10-61
 
< 0.1%
3.461986351 × 10-61
 
< 0.1%
3.608327627 × 10-61
 
< 0.1%
3.63705603 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.9246612953 1
< 0.1%
0.591805651 1
< 0.1%
0.5310062333 1
< 0.1%
0.5280034462 1
< 0.1%
0.5265870133 1
< 0.1%
0.4347602149 1
< 0.1%
0.4045726733 1
< 0.1%
0.3761715701 1
< 0.1%
0.3734071985 1
< 0.1%
0.37037789 1
< 0.1%

tw.education
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct731
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0079097838
Minimum-5.8391923 × 10-6
Maximum0.54827931
Zeros2452
Zeros (%)77.1%
Negative2
Negative (%)0.1%
Memory size25.0 KiB
2023-08-28T14:33:08.662217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-5.8391923 × 10-6
5-th percentile0
Q10
median0
Q30
95-th percentile0.030693357
Maximum0.54827931
Range0.54828515
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.042018773
Coefficient of variation (CV)5.3122531
Kurtosis76.543052
Mean0.0079097838
Median Absolute Deviation (MAD)0
Skewness8.2572456
Sum25.168932
Variance0.0017655773
MonotonicityNot monotonic
2023-08-28T14:33:08.780806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2452
77.1%
4.183672831 × 10-51
 
< 0.1%
0.003290935466 1
 
< 0.1%
2.904395452 × 10-51
 
< 0.1%
0.0004590353116 1
 
< 0.1%
1.045759417 × 10-51
 
< 0.1%
0.05763411017 1
 
< 0.1%
0.005648319277 1
 
< 0.1%
5.119509404 × 10-51
 
< 0.1%
0.0115446058 1
 
< 0.1%
Other values (721) 721
 
22.7%
ValueCountFrequency (%)
-5.839192258 × 10-61
 
< 0.1%
-3.037575066 × 10-61
 
< 0.1%
0 2452
77.1%
3.552849941 × 10-61
 
< 0.1%
3.904940448 × 10-61
 
< 0.1%
4.960694403 × 10-61
 
< 0.1%
5.396192301 × 10-61
 
< 0.1%
5.400586802 × 10-61
 
< 0.1%
5.712932505 × 10-61
 
< 0.1%
5.834846505 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.5482793094 1
< 0.1%
0.4893619542 1
< 0.1%
0.4865643219 1
< 0.1%
0.4769381452 1
< 0.1%
0.4728616949 1
< 0.1%
0.4721266828 1
< 0.1%
0.4670362917 1
< 0.1%
0.4550996576 1
< 0.1%
0.4469093154 1
< 0.1%
0.446232956 1
< 0.1%

tw.energy
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct578
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00819262
Minimum0
Maximum0.51376273
Zeros2605
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:08.893182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.021688729
Maximum0.51376273
Range0.51376273
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04230683
Coefficient of variation (CV)5.1640172
Kurtosis49.380285
Mean0.00819262
Median Absolute Deviation (MAD)0
Skewness6.6960164
Sum26.068917
Variance0.0017898679
MonotonicityNot monotonic
2023-08-28T14:33:09.015649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2605
81.9%
0.001572537462 1
 
< 0.1%
2.789216917 × 10-51
 
< 0.1%
0.008088816374 1
 
< 0.1%
0.001030042598 1
 
< 0.1%
0.02146653539 1
 
< 0.1%
0.0004775398678 1
 
< 0.1%
2.921451274 × 10-51
 
< 0.1%
8.093774798 × 10-51
 
< 0.1%
5.798194667 × 10-51
 
< 0.1%
Other values (568) 568
 
17.9%
ValueCountFrequency (%)
0 2605
81.9%
2.548051649 × 10-61
 
< 0.1%
3.585673637 × 10-61
 
< 0.1%
4.609424074 × 10-61
 
< 0.1%
4.698378867 × 10-61
 
< 0.1%
5.06326617 × 10-61
 
< 0.1%
5.171779846 × 10-61
 
< 0.1%
5.701614343 × 10-61
 
< 0.1%
5.890794666 × 10-61
 
< 0.1%
7.04125197 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.513762731 1
< 0.1%
0.4628362108 1
< 0.1%
0.4597241 1
< 0.1%
0.4284659803 1
< 0.1%
0.4078380208 1
< 0.1%
0.396957564 1
< 0.1%
0.3908683115 1
< 0.1%
0.3634988308 1
< 0.1%
0.3543735333 1
< 0.1%
0.3513677984 1
< 0.1%

tw.environment
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct643
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0083886314
Minimum-5.0647958 × 10-5
Maximum0.53073318
Zeros2540
Zeros (%)79.8%
Negative2
Negative (%)0.1%
Memory size25.0 KiB
2023-08-28T14:33:09.128736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-5.0647958 × 10-5
5-th percentile0
Q10
median0
Q30
95-th percentile0.038919399
Maximum0.53073318
Range0.53078382
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.039667391
Coefficient of variation (CV)4.7287082
Kurtosis64.744146
Mean0.0083886314
Median Absolute Deviation (MAD)0
Skewness7.4037011
Sum26.692625
Variance0.0015735019
MonotonicityNot monotonic
2023-08-28T14:33:09.251162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2540
79.8%
0.00512861337 1
 
< 0.1%
0.07310425006 1
 
< 0.1%
0.03569809042 1
 
< 0.1%
0.001702845032 1
 
< 0.1%
0.004299786429 1
 
< 0.1%
0.001784023868 1
 
< 0.1%
0.059961037 1
 
< 0.1%
0.006433051723 1
 
< 0.1%
0.00842462842 1
 
< 0.1%
Other values (633) 633
 
19.9%
ValueCountFrequency (%)
-5.064795794 × 10-51
 
< 0.1%
-1.279452012 × 10-51
 
< 0.1%
0 2540
79.8%
5.972766042 × 10-61
 
< 0.1%
6.012685694 × 10-61
 
< 0.1%
8.020404736 × 10-61
 
< 0.1%
8.183111169 × 10-61
 
< 0.1%
8.299634018 × 10-61
 
< 0.1%
8.872323603 × 10-61
 
< 0.1%
1.08880823 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.5307331757 1
< 0.1%
0.5296030549 1
< 0.1%
0.4883196872 1
< 0.1%
0.4371419681 1
< 0.1%
0.413865492 1
< 0.1%
0.3960175322 1
< 0.1%
0.3865341757 1
< 0.1%
0.3851702304 1
< 0.1%
0.384013839 1
< 0.1%
0.3566659452 1
< 0.1%

tw.fair.elections
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct285
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0019957059
Minimum0
Maximum0.37973321
Zeros2898
Zeros (%)91.1%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:09.404310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.00035029394
Maximum0.37973321
Range0.37973321
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.018890418
Coefficient of variation (CV)9.4655319
Kurtosis175.94245
Mean0.0019957059
Median Absolute Deviation (MAD)0
Skewness12.568535
Sum6.3503361
Variance0.00035684788
MonotonicityNot monotonic
2023-08-28T14:33:09.519511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2898
91.1%
3.140937508 × 10-51
 
< 0.1%
0.07837690679 1
 
< 0.1%
1.981780366 × 10-51
 
< 0.1%
6.226245731 × 10-61
 
< 0.1%
0.003259754448 1
 
< 0.1%
0.0001819688315 1
 
< 0.1%
0.2333867823 1
 
< 0.1%
0.2195627267 1
 
< 0.1%
0.1477442887 1
 
< 0.1%
Other values (275) 275
 
8.6%
ValueCountFrequency (%)
0 2898
91.1%
2.321803611 × 10-61
 
< 0.1%
2.527748063 × 10-61
 
< 0.1%
4.362569751 × 10-61
 
< 0.1%
6.226245731 × 10-61
 
< 0.1%
6.658173743 × 10-61
 
< 0.1%
6.739457984 × 10-61
 
< 0.1%
6.832711578 × 10-61
 
< 0.1%
6.931528857 × 10-61
 
< 0.1%
7.308696902 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.3797332129 1
< 0.1%
0.3371434721 1
< 0.1%
0.2827218681 1
< 0.1%
0.260285648 1
< 0.1%
0.2578688116 1
< 0.1%
0.2553661226 1
< 0.1%
0.2461214716 1
< 0.1%
0.2333867823 1
< 0.1%
0.2195627267 1
< 0.1%
0.2087400555 1
< 0.1%

tw.federal.agencies.and.gov.regulation
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1585
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.025250935
Minimum-0.00011545225
Maximum0.72308861
Zeros1598
Zeros (%)50.2%
Negative1
Negative (%)< 0.1%
Memory size25.0 KiB
2023-08-28T14:33:09.639709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.00011545225
5-th percentile0
Q10
median0
Q30.01675356
95-th percentile0.14455342
Maximum0.72308861
Range0.72320406
Interquartile range (IQR)0.01675356

Descriptive statistics

Standard deviation0.06266357
Coefficient of variation (CV)2.4816337
Kurtosis23.578157
Mean0.025250935
Median Absolute Deviation (MAD)0
Skewness4.1586735
Sum80.348474
Variance0.003926723
MonotonicityNot monotonic
2023-08-28T14:33:09.749574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1598
50.2%
0.03147230964 1
 
< 0.1%
0.0001853183817 1
 
< 0.1%
0.120320846 1
 
< 0.1%
0.02117111656 1
 
< 0.1%
1.954306687 × 10-51
 
< 0.1%
0.2029172936 1
 
< 0.1%
0.0002679298626 1
 
< 0.1%
0.05601397056 1
 
< 0.1%
0.1347865069 1
 
< 0.1%
Other values (1575) 1575
49.5%
ValueCountFrequency (%)
-0.0001154522469 1
 
< 0.1%
0 1598
50.2%
2.587481966 × 10-71
 
< 0.1%
2.988977438 × 10-61
 
< 0.1%
3.928346286 × 10-61
 
< 0.1%
5.171456931 × 10-61
 
< 0.1%
5.274520869 × 10-61
 
< 0.1%
5.748550391 × 10-61
 
< 0.1%
5.831581366 × 10-61
 
< 0.1%
6.258362611 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.7230886116 1
< 0.1%
0.5837360672 1
< 0.1%
0.582608871 1
< 0.1%
0.5679453164 1
< 0.1%
0.5640002026 1
< 0.1%
0.5574342679 1
< 0.1%
0.5511242473 1
< 0.1%
0.4387033944 1
< 0.1%
0.4332868271 1
< 0.1%
0.4078523464 1
< 0.1%

tw.guns
Real number (ℝ)

Distinct157
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0012732691
Minimum0
Maximum0.49180377
Zeros3026
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:09.863038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.49180377
Range0.49180377
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.017342067
Coefficient of variation (CV)13.620111
Kurtosis433.19628
Mean0.0012732691
Median Absolute Deviation (MAD)0
Skewness19.485874
Sum4.0515423
Variance0.00030074729
MonotonicityNot monotonic
2023-08-28T14:33:09.979204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3026
95.1%
0.001081371534 1
 
< 0.1%
0.0002812262419 1
 
< 0.1%
5.039775052 × 10-61
 
< 0.1%
0.1780468023 1
 
< 0.1%
0.07943212769 1
 
< 0.1%
6.731099204 × 10-61
 
< 0.1%
0.0007322270738 1
 
< 0.1%
0.002778165056 1
 
< 0.1%
0.3448581205 1
 
< 0.1%
Other values (147) 147
 
4.6%
ValueCountFrequency (%)
0 3026
95.1%
1.261481303 × 10-61
 
< 0.1%
1.705000978 × 10-61
 
< 0.1%
1.844191227 × 10-61
 
< 0.1%
2.427051386 × 10-61
 
< 0.1%
2.557970412 × 10-61
 
< 0.1%
2.903412429 × 10-61
 
< 0.1%
3.977289845 × 10-61
 
< 0.1%
4.444938507 × 10-61
 
< 0.1%
5.039775052 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.4918037662 1
< 0.1%
0.4068052241 1
< 0.1%
0.3634091715 1
< 0.1%
0.3448581205 1
< 0.1%
0.218951857 1
< 0.1%
0.2143087926 1
< 0.1%
0.1780468023 1
< 0.1%
0.1769757138 1
< 0.1%
0.1730695118 1
< 0.1%
0.1720400699 1
< 0.1%

tw.healthcare
Real number (ℝ)

Distinct721
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0093279987
Minimum0
Maximum0.48747762
Zeros2462
Zeros (%)77.4%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:10.093857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.045278522
Maximum0.48747762
Range0.48747762
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.042413598
Coefficient of variation (CV)4.5469129
Kurtosis40.609607
Mean0.0093279987
Median Absolute Deviation (MAD)0
Skewness6.0414973
Sum29.681692
Variance0.0017989133
MonotonicityNot monotonic
2023-08-28T14:33:10.210117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2462
77.4%
1.919579247 × 10-51
 
< 0.1%
0.05069753243 1
 
< 0.1%
2.355984149 × 10-51
 
< 0.1%
0.01693149411 1
 
< 0.1%
1.089802186 × 10-51
 
< 0.1%
1.034407804 × 10-51
 
< 0.1%
0.3558648538 1
 
< 0.1%
0.3406894406 1
 
< 0.1%
0.001962736509 1
 
< 0.1%
Other values (711) 711
 
22.3%
ValueCountFrequency (%)
0 2462
77.4%
8.040738941 × 10-71
 
< 0.1%
3.182447697 × 10-61
 
< 0.1%
3.783464182 × 10-61
 
< 0.1%
3.785175946 × 10-61
 
< 0.1%
5.04113686 × 10-61
 
< 0.1%
5.14068562 × 10-61
 
< 0.1%
5.539713875 × 10-61
 
< 0.1%
6.90429549 × 10-61
 
< 0.1%
7.420771457 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.4874776242 1
< 0.1%
0.449834929 1
< 0.1%
0.4113789128 1
< 0.1%
0.382160364 1
< 0.1%
0.377152674 1
< 0.1%
0.3558648538 1
< 0.1%
0.3471340388 1
< 0.1%
0.3449842789 1
< 0.1%
0.3446505104 1
< 0.1%
0.3411786291 1
< 0.1%

tw.higher.education
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct437
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0062898065
Minimum-3.6055369 × 10-6
Maximum0.53636641
Zeros2746
Zeros (%)86.3%
Negative1
Negative (%)< 0.1%
Memory size25.0 KiB
2023-08-28T14:33:10.321474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-3.6055369 × 10-6
5-th percentile0
Q10
median0
Q30
95-th percentile0.0072275323
Maximum0.53636641
Range0.53637001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.039089694
Coefficient of variation (CV)6.2147689
Kurtosis69.38643
Mean0.0062898065
Median Absolute Deviation (MAD)0
Skewness7.9238192
Sum20.014164
Variance0.0015280042
MonotonicityNot monotonic
2023-08-28T14:33:10.446229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2746
86.3%
0.0008660997347 1
 
< 0.1%
0.0157473334 1
 
< 0.1%
3.251416857 × 10-51
 
< 0.1%
5.032177672 × 10-51
 
< 0.1%
3.841343483 × 10-51
 
< 0.1%
0.000241148613 1
 
< 0.1%
0.001426061676 1
 
< 0.1%
0.02169984019 1
 
< 0.1%
3.865154994 × 10-51
 
< 0.1%
Other values (427) 427
 
13.4%
ValueCountFrequency (%)
-3.605536927 × 10-61
 
< 0.1%
0 2746
86.3%
1.205001701 × 10-61
 
< 0.1%
3.95616593 × 10-61
 
< 0.1%
4.933741783 × 10-61
 
< 0.1%
5.186558496 × 10-61
 
< 0.1%
5.873409818 × 10-61
 
< 0.1%
7.819042897 × 10-61
 
< 0.1%
7.941220258 × 10-61
 
< 0.1%
8.398654812 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.5363664091 1
< 0.1%
0.4564080259 1
< 0.1%
0.4443583445 1
< 0.1%
0.4372340183 1
< 0.1%
0.4357769772 1
< 0.1%
0.4133492367 1
< 0.1%
0.4086580322 1
< 0.1%
0.4039835955 1
< 0.1%
0.369095026 1
< 0.1%
0.362778305 1
< 0.1%

tw.immigration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct348
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0029444168
Minimum0
Maximum0.58703065
Zeros2835
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:10.561062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0042138896
Maximum0.58703065
Range0.58703065
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.025243189
Coefficient of variation (CV)8.573239
Kurtosis237.644
Mean0.0029444168
Median Absolute Deviation (MAD)0
Skewness14.070558
Sum9.3691343
Variance0.00063721859
MonotonicityNot monotonic
2023-08-28T14:33:10.825868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2835
89.1%
0.09709057737 1
 
< 0.1%
0.0004468919116 1
 
< 0.1%
0.0008300486025 1
 
< 0.1%
0.01056293393 1
 
< 0.1%
0.0004692939173 1
 
< 0.1%
0.5271689849 1
 
< 0.1%
0.3787852645 1
 
< 0.1%
0.0008101251756 1
 
< 0.1%
7.054369139 × 10-61
 
< 0.1%
Other values (338) 338
 
10.6%
ValueCountFrequency (%)
0 2835
89.1%
3.142090557 × 10-61
 
< 0.1%
3.611769602 × 10-61
 
< 0.1%
5.384154996 × 10-61
 
< 0.1%
5.803507704 × 10-61
 
< 0.1%
6.954846273 × 10-61
 
< 0.1%
7.054369139 × 10-61
 
< 0.1%
7.383643441 × 10-61
 
< 0.1%
7.97304867 × 10-61
 
< 0.1%
8.230545051 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.5870306482 1
< 0.1%
0.5271689849 1
< 0.1%
0.3961683301 1
< 0.1%
0.3787852645 1
< 0.1%
0.3779678583 1
< 0.1%
0.3729922869 1
< 0.1%
0.2937913029 1
< 0.1%
0.2898151314 1
< 0.1%
0.2655519969 1
< 0.1%
0.2492342323 1
< 0.1%

tw.indian.affairs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct417
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0031736131
Minimum0
Maximum0.36080377
Zeros2766
Zeros (%)86.9%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:10.944374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0073560738
Maximum0.36080377
Range0.36080377
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.023138055
Coefficient of variation (CV)7.2907611
Kurtosis128.28256
Mean0.0031736131
Median Absolute Deviation (MAD)0
Skewness10.793005
Sum10.098437
Variance0.00053536958
MonotonicityNot monotonic
2023-08-28T14:33:11.052138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2766
86.9%
0.02021082425 1
 
< 0.1%
0.01329359229 1
 
< 0.1%
2.976701826 × 10-51
 
< 0.1%
1.332478072 × 10-51
 
< 0.1%
4.204288373 × 10-51
 
< 0.1%
3.391206753 × 10-51
 
< 0.1%
0.007986433672 1
 
< 0.1%
0.2645432386 1
 
< 0.1%
0.01175942956 1
 
< 0.1%
Other values (407) 407
 
12.8%
ValueCountFrequency (%)
0 2766
86.9%
3.75171951 × 10-61
 
< 0.1%
5.533394136 × 10-61
 
< 0.1%
7.084445433 × 10-61
 
< 0.1%
7.790216921 × 10-61
 
< 0.1%
8.406428664 × 10-61
 
< 0.1%
9.574317047 × 10-61
 
< 0.1%
1.058639724 × 10-51
 
< 0.1%
1.099792801 × 10-51
 
< 0.1%
1.108249189 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.3608037749 1
< 0.1%
0.3600410354 1
< 0.1%
0.3448154865 1
< 0.1%
0.3141439039 1
< 0.1%
0.2974346332 1
< 0.1%
0.2952472224 1
< 0.1%
0.2867082784 1
< 0.1%
0.2806817837 1
< 0.1%
0.2649817036 1
< 0.1%
0.2645432386 1
< 0.1%

tw.intelligence.and.surveillance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct588
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0063327625
Minimum0
Maximum0.50145918
Zeros2595
Zeros (%)81.6%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:11.170122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.025174618
Maximum0.50145918
Range0.50145918
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.033373989
Coefficient of variation (CV)5.2700523
Kurtosis84.466845
Mean0.0063327625
Median Absolute Deviation (MAD)0
Skewness8.4826945
Sum20.15085
Variance0.0011138232
MonotonicityNot monotonic
2023-08-28T14:33:11.288882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2595
81.6%
0.01023555476 1
 
< 0.1%
0.005313472636 1
 
< 0.1%
0.1333321194 1
 
< 0.1%
0.002011523541 1
 
< 0.1%
0.004298445838 1
 
< 0.1%
0.06611334183 1
 
< 0.1%
0.002181200303 1
 
< 0.1%
3.340261247 × 10-51
 
< 0.1%
0.03737086225 1
 
< 0.1%
Other values (578) 578
 
18.2%
ValueCountFrequency (%)
0 2595
81.6%
2.453981041 × 10-61
 
< 0.1%
6.601578707 × 10-61
 
< 0.1%
8.887661791 × 10-61
 
< 0.1%
9.921254069 × 10-61
 
< 0.1%
1.084480218 × 10-51
 
< 0.1%
1.106451652 × 10-51
 
< 0.1%
1.127086857 × 10-51
 
< 0.1%
1.128611952 × 10-51
 
< 0.1%
1.133394505 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.5014591825 1
< 0.1%
0.4450098067 1
< 0.1%
0.4352183572 1
< 0.1%
0.4329274277 1
< 0.1%
0.3861211362 1
< 0.1%
0.3821205268 1
< 0.1%
0.361080265 1
< 0.1%
0.3414081811 1
< 0.1%
0.3274392926 1
< 0.1%
0.323961027 1
< 0.1%

tw.labor
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct406
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0030587475
Minimum0
Maximum0.27025049
Zeros2777
Zeros (%)87.3%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:11.399061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0067510283
Maximum0.27025049
Range0.27025049
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.020736709
Coefficient of variation (CV)6.7794771
Kurtosis93.716445
Mean0.0030587475
Median Absolute Deviation (MAD)0
Skewness9.3148202
Sum9.7329346
Variance0.00043001109
MonotonicityNot monotonic
2023-08-28T14:33:11.514566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2777
87.3%
0.2699212852 1
 
< 0.1%
2.747620821 × 10-51
 
< 0.1%
4.68138357 × 10-61
 
< 0.1%
0.0002416299828 1
 
< 0.1%
0.0103367989 1
 
< 0.1%
0.004215951502 1
 
< 0.1%
0.1902543291 1
 
< 0.1%
0.002561578402 1
 
< 0.1%
0.003229856783 1
 
< 0.1%
Other values (396) 396
 
12.4%
ValueCountFrequency (%)
0 2777
87.3%
4.291673597 × 10-61
 
< 0.1%
4.68138357 × 10-61
 
< 0.1%
5.293609145 × 10-61
 
< 0.1%
5.431314674 × 10-61
 
< 0.1%
5.489495038 × 10-61
 
< 0.1%
5.744901224 × 10-61
 
< 0.1%
6.579519984 × 10-61
 
< 0.1%
6.711532078 × 10-61
 
< 0.1%
8.046936253 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.2702504916 1
< 0.1%
0.2699212852 1
< 0.1%
0.2664372108 1
< 0.1%
0.2565144942 1
< 0.1%
0.2435248152 1
< 0.1%
0.2430646047 1
< 0.1%
0.2402893013 1
< 0.1%
0.2369015302 1
< 0.1%
0.2299344427 1
< 0.1%
0.2219860631 1
< 0.1%

tw.law.courts.and.judges
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct854
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0684899
Minimum-6.0778362 × 10-7
Maximum1
Zeros2139
Zeros (%)67.2%
Negative1
Negative (%)< 0.1%
Memory size25.0 KiB
2023-08-28T14:33:11.630723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-6.0778362 × 10-7
5-th percentile0
Q10
median0
Q30.00027503254
95-th percentile1
Maximum1
Range1.0000006
Interquartile range (IQR)0.00027503254

Descriptive statistics

Standard deviation0.23852132
Coefficient of variation (CV)3.4825765
Kurtosis11.024248
Mean0.0684899
Median Absolute Deviation (MAD)0
Skewness3.5767492
Sum217.93486
Variance0.056892418
MonotonicityNot monotonic
2023-08-28T14:33:11.744429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2139
67.2%
1 191
 
6.0%
5.869320102 × 10-51
 
< 0.1%
0.003581364252 1
 
< 0.1%
0.003560638303 1
 
< 0.1%
0.0003264302979 1
 
< 0.1%
0.02353406615 1
 
< 0.1%
3.885609159 × 10-51
 
< 0.1%
0.0576272502 1
 
< 0.1%
0.006473263277 1
 
< 0.1%
Other values (844) 844
 
26.5%
ValueCountFrequency (%)
-6.077836241 × 10-71
 
< 0.1%
0 2139
67.2%
3.00090058 × 10-61
 
< 0.1%
3.122649103 × 10-61
 
< 0.1%
4.344148183 × 10-61
 
< 0.1%
4.767160806 × 10-61
 
< 0.1%
4.997967771 × 10-61
 
< 0.1%
5.539890335 × 10-61
 
< 0.1%
6.569890859 × 10-61
 
< 0.1%
6.789237373 × 10-61
 
< 0.1%
ValueCountFrequency (%)
1 191
6.0%
0.5640057531 1
 
< 0.1%
0.5574687175 1
 
< 0.1%
0.5348946232 1
 
< 0.1%
0.5127428236 1
 
< 0.1%
0.3952788826 1
 
< 0.1%
0.3828679583 1
 
< 0.1%
0.3714642495 1
 
< 0.1%
0.3425912946 1
 
< 0.1%
0.3418125748 1
 
< 0.1%

tw.transportation
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct551
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0085344878
Minimum0
Maximum0.39543985
Zeros2632
Zeros (%)82.7%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:11.863654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.03164216
Maximum0.39543985
Range0.39543985
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03992263
Coefficient of variation (CV)4.6778003
Kurtosis33.680809
Mean0.0085344878
Median Absolute Deviation (MAD)0
Skewness5.6616162
Sum27.15674
Variance0.0015938164
MonotonicityNot monotonic
2023-08-28T14:33:11.986059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2632
82.7%
0.01575976197 1
 
< 0.1%
7.363014765 × 10-61
 
< 0.1%
0.0091729255 1
 
< 0.1%
0.02089105284 1
 
< 0.1%
0.01148158249 1
 
< 0.1%
0.3370484792 1
 
< 0.1%
0.0001034406786 1
 
< 0.1%
0.2012681493 1
 
< 0.1%
0.0009901066645 1
 
< 0.1%
Other values (541) 541
 
17.0%
ValueCountFrequency (%)
0 2632
82.7%
2.951338194 × 10-61
 
< 0.1%
3.060770631 × 10-61
 
< 0.1%
5.727291779 × 10-61
 
< 0.1%
6.648366502 × 10-61
 
< 0.1%
7.363014765 × 10-61
 
< 0.1%
7.552352219 × 10-61
 
< 0.1%
7.619977901 × 10-61
 
< 0.1%
8.38905065 × 10-61
 
< 0.1%
8.528758852 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.3954398487 1
< 0.1%
0.3457123304 1
< 0.1%
0.3370484792 1
< 0.1%
0.3350447061 1
< 0.1%
0.329912055 1
< 0.1%
0.3280361488 1
< 0.1%
0.3276350024 1
< 0.1%
0.3237293354 1
< 0.1%
0.3150025273 1
< 0.1%
0.305487485 1
< 0.1%

tw.veterans.affairs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct439
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0079575322
Minimum0
Maximum0.48789226
Zeros2744
Zeros (%)86.2%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:12.112309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.01945285
Maximum0.48789226
Range0.48789226
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.043344926
Coefficient of variation (CV)5.4470313
Kurtosis56.787458
Mean0.0079575322
Median Absolute Deviation (MAD)0
Skewness7.1408114
Sum25.320867
Variance0.0018787826
MonotonicityNot monotonic
2023-08-28T14:33:12.227072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2744
86.2%
0.005167012998 1
 
< 0.1%
0.0414087246 1
 
< 0.1%
0.07133172777 1
 
< 0.1%
0.4185933568 1
 
< 0.1%
0.0001437077162 1
 
< 0.1%
0.3037057337 1
 
< 0.1%
0.3753409112 1
 
< 0.1%
0.3916088141 1
 
< 0.1%
0.2743647242 1
 
< 0.1%
Other values (429) 429
 
13.5%
ValueCountFrequency (%)
0 2744
86.2%
2.020127364 × 10-61
 
< 0.1%
2.053935543 × 10-61
 
< 0.1%
2.128407985 × 10-61
 
< 0.1%
2.468019903 × 10-61
 
< 0.1%
2.523301006 × 10-61
 
< 0.1%
2.764872505 × 10-61
 
< 0.1%
4.998745887 × 10-61
 
< 0.1%
6.534372061 × 10-61
 
< 0.1%
6.564529548 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.4878922628 1
< 0.1%
0.4755314107 1
< 0.1%
0.4736138468 1
< 0.1%
0.4713979676 1
< 0.1%
0.4433202812 1
< 0.1%
0.4364898788 1
< 0.1%
0.4355855305 1
< 0.1%
0.4185933568 1
< 0.1%
0.4085058099 1
< 0.1%
0.4075770586 1
< 0.1%

tw.womens.issues
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct452
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0026720725
Minimum0
Maximum0.51146588
Zeros2731
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size25.0 KiB
2023-08-28T14:33:12.342215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0046730353
Maximum0.51146588
Range0.51146588
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.022458431
Coefficient of variation (CV)8.4048733
Kurtosis221.77476
Mean0.0026720725
Median Absolute Deviation (MAD)0
Skewness13.725912
Sum8.5025346
Variance0.0005043811
MonotonicityNot monotonic
2023-08-28T14:33:12.454285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2731
85.8%
1.210971961 × 10-51
 
< 0.1%
0.0003843184804 1
 
< 0.1%
2.63936972 × 10-51
 
< 0.1%
1.75038281 × 10-51
 
< 0.1%
6.828000174 × 10-51
 
< 0.1%
6.043775255 × 10-51
 
< 0.1%
2.262173557 × 10-51
 
< 0.1%
0.002478354261 1
 
< 0.1%
0.008064833115 1
 
< 0.1%
Other values (442) 442
 
13.9%
ValueCountFrequency (%)
0 2731
85.8%
1.619057884 × 10-61
 
< 0.1%
4.279457029 × 10-61
 
< 0.1%
5.236273925 × 10-61
 
< 0.1%
5.266548938 × 10-61
 
< 0.1%
5.27198272 × 10-61
 
< 0.1%
5.986340375 × 10-61
 
< 0.1%
6.352478763 × 10-61
 
< 0.1%
7.230674002 × 10-61
 
< 0.1%
7.300184961 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.5114658831 1
< 0.1%
0.392621962 1
< 0.1%
0.3859728309 1
< 0.1%
0.3766054331 1
< 0.1%
0.3474910526 1
< 0.1%
0.323966605 1
< 0.1%
0.2957578534 1
< 0.1%
0.2504111083 1
< 0.1%
0.2356087149 1
< 0.1%
0.226480449 1
< 0.1%

Interactions

2023-08-28T14:33:01.610153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:51.856062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:54.468459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:57.148923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:59.663562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:02.281932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:05.096659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:07.842391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:10.498378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:13.147211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:15.947818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:18.473209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:21.135749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:23.673063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:26.418431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:28.985904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:31.853508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:34.630994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:37.340474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:39.907226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:42.528045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:45.286711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:47.999207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:50.760201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:53.362864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:56.099973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:58.844767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:33:01.704339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-08-28T14:32:10.124276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:12.743014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:15.548301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:18.085785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:20.769905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:23.286184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:26.041246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:28.591352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:31.439074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:34.204117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:36.802149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:39.523459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:42.141610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:44.895160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:47.590922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:50.366781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:52.963622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:55.719203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:58.438900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:33:01.228467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:33:03.910895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:54.176804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:56.722600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:59.366644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:01.988460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:04.801874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:07.536074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:10.212576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:12.835262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:15.646696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:18.183033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:20.857624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:23.388292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:26.132760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:28.684025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:31.538708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:34.304478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:36.892881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:39.615787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:42.235628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:44.992622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:47.689129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:50.459061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:53.055943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:55.810086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:58.540677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:33:01.322651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:33:04.013655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:54.275910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:56.822870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:59.467064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:02.090591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:04.908548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:07.644799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:10.310364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:12.941497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:15.762684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:18.289807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:20.956915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:23.490249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:26.234658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:28.786529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:31.644970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:34.428667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:36.998310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:39.724644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:42.343590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:45.097829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:47.804327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:50.573512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:53.166047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:55.915593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:58.646939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:33:01.430610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:33:04.103460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:54.376679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:56.911007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:31:59.568412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:02.190310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:05.002695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:07.748814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:10.410427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:13.043999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:15.854166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:18.384568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:21.045884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:23.581212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:26.325903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:28.880956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:31.758332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:34.534998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:37.090699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:39.816979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:42.437080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:45.194140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:47.900236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:50.668187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:53.260601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:56.009641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:32:58.747035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-08-28T14:33:01.519455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-08-28T14:33:12.582425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
yearcongnotw.latent1tw.abortion.and.social.conservatismtw.agriculturetw.banking.and.financetw.civil.rightstw.congress.and.proceduraltw.crimetw.defense.and.foreign.policytw.economytw.educationtw.energytw.environmenttw.fair.electionstw.federal.agencies.and.gov.regulationtw.gunstw.healthcaretw.higher.educationtw.immigrationtw.indian.affairstw.intelligence.and.surveillancetw.labortw.law.courts.and.judgestw.transportationtw.veterans.affairstw.womens.issues
year1.0000.9880.001-0.174-0.117-0.086-0.165-0.188-0.150-0.155-0.094-0.101-0.048-0.064-0.076-0.051-0.002-0.109-0.129-0.097-0.104-0.124-0.139-0.105-0.131-0.044-0.118
congno0.9881.0000.011-0.170-0.119-0.084-0.161-0.183-0.147-0.154-0.089-0.097-0.048-0.064-0.071-0.043-0.003-0.108-0.127-0.091-0.101-0.122-0.136-0.113-0.132-0.043-0.115
tw.latent10.0010.0111.000-0.363-0.285-0.301-0.441-0.360-0.413-0.393-0.394-0.322-0.284-0.313-0.255-0.396-0.225-0.271-0.263-0.287-0.296-0.329-0.276-0.555-0.269-0.282-0.332
tw.abortion.and.social.conservatism-0.174-0.170-0.3631.0000.4200.4140.5520.4330.5940.3530.4780.4220.4190.4230.4320.4390.3620.4650.3800.5100.5580.5840.4770.4820.3880.4090.549
tw.agriculture-0.117-0.119-0.2850.4201.0000.4720.3830.3780.3730.3390.4020.5090.5060.5780.4000.3650.3780.4520.5340.5030.4620.4490.6000.2890.5010.4410.524
tw.banking.and.finance-0.086-0.084-0.3010.4140.4721.0000.4060.3930.4330.3150.5520.4610.4720.4630.3170.4960.2900.4270.3880.4400.3990.5800.4880.2890.4650.3870.487
tw.civil.rights-0.165-0.161-0.4410.5520.3830.4061.0000.3840.5400.5400.4450.4340.4220.4570.3900.4170.3320.3770.3760.4520.5090.5010.4510.3440.3880.4180.523
tw.congress.and.procedural-0.188-0.183-0.3600.4330.3780.3930.3841.0000.5030.3780.5180.3390.3710.3740.3550.6600.2430.4470.2990.3640.3710.4420.3880.2940.3930.3420.392
tw.crime-0.150-0.147-0.4130.5940.3730.4330.5400.5031.0000.4070.5830.3910.3920.3870.3590.5010.3260.4170.3200.4590.4320.5660.4380.4150.3900.3890.484
tw.defense.and.foreign.policy-0.155-0.154-0.3930.3530.3390.3150.5400.3780.4071.0000.3450.3270.3610.3180.2910.3590.2790.3110.2950.4060.2940.3790.3260.2110.3710.4450.329
tw.economy-0.094-0.089-0.3940.4780.4020.5520.4450.5180.5830.3451.0000.4300.4330.4270.3250.6810.2660.4700.3300.3910.3990.5130.4160.2970.4130.3740.435
tw.education-0.101-0.097-0.3220.4220.5090.4610.4340.3390.3910.3270.4301.0000.5350.4590.3520.3710.2970.4850.6910.4550.4500.4650.4790.2430.4460.4440.521
tw.energy-0.048-0.048-0.2840.4190.5060.4720.4220.3710.3920.3610.4330.5351.0000.6460.3600.4090.3440.4010.4290.4590.4980.4980.4810.3060.5040.4270.490
tw.environment-0.064-0.064-0.3130.4230.5780.4630.4570.3740.3870.3180.4270.4590.6461.0000.4010.4140.3370.4020.4070.4480.5300.4370.4990.2910.5200.4480.496
tw.fair.elections-0.076-0.071-0.2550.4320.4000.3170.3900.3550.3590.2910.3250.3520.3600.4011.0000.3150.4030.3500.3830.4260.4570.4150.3850.2850.3530.3800.509
tw.federal.agencies.and.gov.regulation-0.051-0.043-0.3960.4390.3650.4960.4170.6600.5010.3590.6810.3710.4090.4140.3151.0000.2440.3910.2990.3590.3590.5100.3820.2900.3850.3400.399
tw.guns-0.002-0.003-0.2250.3620.3780.2900.3320.2430.3260.2790.2660.2970.3440.3370.4030.2441.0000.2820.3250.4170.3930.3590.3560.2430.3850.3400.391
tw.healthcare-0.109-0.108-0.2710.4650.4520.4270.3770.4470.4170.3110.4700.4850.4010.4020.3500.3910.2821.0000.3900.4460.4400.4870.4900.3140.4240.4900.495
tw.higher.education-0.129-0.127-0.2630.3800.5340.3880.3760.2990.3200.2950.3300.6910.4290.4070.3830.2990.3250.3901.0000.4560.4580.4390.4420.2420.4190.4230.488
tw.immigration-0.097-0.091-0.2870.5100.5030.4400.4520.3640.4590.4060.3910.4550.4590.4480.4260.3590.4170.4460.4561.0000.4820.5160.6310.3430.4870.4770.566
tw.indian.affairs-0.104-0.101-0.2960.5580.4620.3990.5090.3710.4320.2940.3990.4500.4980.5300.4570.3590.3930.4400.4580.4821.0000.4670.4590.3390.4520.5040.534
tw.intelligence.and.surveillance-0.124-0.122-0.3290.5840.4490.5800.5010.4420.5660.3790.5130.4650.4980.4370.4150.5100.3590.4870.4390.5160.4671.0000.4740.3900.4730.4360.552
tw.labor-0.139-0.136-0.2760.4770.6000.4880.4510.3880.4380.3260.4160.4790.4810.4990.3850.3820.3560.4900.4420.6310.4590.4741.0000.3620.5090.4430.568
tw.law.courts.and.judges-0.105-0.113-0.5550.4820.2890.2890.3440.2940.4150.2110.2970.2430.3060.2910.2850.2900.2430.3140.2420.3430.3390.3900.3621.0000.2860.2480.331
tw.transportation-0.131-0.132-0.2690.3880.5010.4650.3880.3930.3900.3710.4130.4460.5040.5200.3530.3850.3850.4240.4190.4870.4520.4730.5090.2861.0000.4420.597
tw.veterans.affairs-0.044-0.043-0.2820.4090.4410.3870.4180.3420.3890.4450.3740.4440.4270.4480.3800.3400.3400.4900.4230.4770.5040.4360.4430.2480.4421.0000.510
tw.womens.issues-0.118-0.115-0.3320.5490.5240.4870.5230.3920.4840.3290.4350.5210.4900.4960.5090.3990.3910.4950.4880.5660.5340.5520.5680.3310.5970.5101.000

Missing values

2023-08-28T14:33:04.283108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-28T14:33:04.711216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-28T14:33:04.944594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

bill.idyeardatebill.strbill.desccongnosponsorscosponsorstw.latent1tw.abortion.and.social.conservatismtw.agriculturetw.banking.and.financetw.civil.rightstw.congress.and.proceduraltw.crimetw.defense.and.foreign.policytw.economytw.educationtw.energytw.environmenttw.fair.electionstw.federal.agencies.and.gov.regulationtw.gunstw.healthcaretw.higher.educationtw.immigrationtw.indian.affairstw.intelligence.and.surveillancetw.labortw.law.courts.and.judgestw.transportationtw.veterans.affairstw.womens.issues
0113_hr8320142014-12-13H.R. 83|On the Motion to Proceed H.R. 83|To require the Secretary of the Interior to assemble a team of technical, policy, and financial experts to address the energy needs of the insular areas of the United States and the Freely Associated States through the development of energy action plans aimed at promoting access to affordable, reliable energy, including increasing use of indigenous clean-energy resources, and for other purposes.To require the Secretary of the Interior to assemble a team of technical, policy, and financial experts to address the energy needs of the insular areas of the United States and the Freely Associated States through the development of energy action plans aimed at promoting access to affordable, reliable energy, including increasing use of indigenous clean-energy resources, and for other purposes.113NaNNaN0.4717850.00.00.00.0726410.1106860.0661930.0132350.0900170.0449130.0679330.0007810.00.0019930.00.00.00.00.00.00.00.00.00.00.0
1113_pn107020142014-12-13PN1070|On the Motion to Proceed PN1070|Christopher Smith, of Texas, to be an Assistant Secretary of Energy (Fossil Energy)Christopher Smith, of Texas, to be an Assistant Secretary of Energy (Fossil Energy)113NaNNaN1.0000000.00.00.00.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00.0000000.00.00.00.00.00.00.00.00.00.00.0
2113_pn109920142014-12-13PN1099|On the Motion to Proceed PN1099|Frank A. Rose, of Massachusetts, to be an Assistant Secretary of State (Verification and Compliance)Frank A. Rose, of Massachusetts, to be an Assistant Secretary of State (Verification and Compliance)113NaNNaN1.0000000.00.00.00.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00.0000000.00.00.00.00.00.00.00.00.00.00.0
3113_pn116020142014-12-13PN1160|On the Motion to Proceed PN1160|Vivek Hallegere Murthy, of Massachusetts, to be Medical Director in the Regular Corps of the Public Health Service, subject to qualifications therefor as provided by law and regulations, and to be Surgeon General of the Public Health Service for a term of four yearsVivek Hallegere Murthy, of Massachusetts, to be Medical Director in the Regular Corps of the Public Health Service, subject to qualifications therefor as provided by law and regulations, and to be Surgeon General of the Public Health Service for a term of four years113NaNNaN1.0000000.00.00.00.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00.0000000.00.00.00.00.00.00.00.00.00.00.0
4113_pn129720142014-12-13PN1297|On the Motion to Proceed PN1297|John Charles Cruden, of Virginia, to be an Assistant Attorney GeneralJohn Charles Cruden, of Virginia, to be an Assistant Attorney General113NaNNaN1.0000000.00.00.00.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00.0000000.00.00.00.00.00.00.00.00.00.00.0
5113_pn134520142014-12-13PN1345|On the Motion to Proceed PN1345|Stephen R. Bough, of Missouri, to be United States District Judge for the Western District of MissouriStephen R. Bough, of Missouri, to be United States District Judge for the Western District of Missouri113NaNNaN0.0000000.00.00.00.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00.0000000.00.00.00.00.00.00.01.00.00.00.0
6113_pn150420142014-12-13PN1504|On the Motion to Proceed PN1504|Estevan R. Lopez, of New Mexico, to be Commissioner of ReclamationEstevan R. Lopez, of New Mexico, to be Commissioner of Reclamation113NaNNaN1.0000000.00.00.00.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00.0000000.00.00.00.00.00.00.00.00.00.00.0
7113_pn156120142014-12-13PN1561|On the Motion to Proceed PN1561|Jonathan Nicholas Stivers, of the District of Columbia, to be an Assistant Administrator of the United States Agency for International DevelopmentJonathan Nicholas Stivers, of the District of Columbia, to be an Assistant Administrator of the United States Agency for International Development113NaNNaN1.0000000.00.00.00.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00.0000000.00.00.00.00.00.00.00.00.00.00.0
8113_pn173220142014-12-13PN1732|On the Motion to Proceed PN1732|Marcus Dwayne Jadotte, of Florida, to be an Assistant Secretary of CommerceMarcus Dwayne Jadotte, of Florida, to be an Assistant Secretary of Commerce113NaNNaN1.0000000.00.00.00.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00.0000000.00.00.00.00.00.00.00.00.00.00.0
9113_pn175320142014-12-13PN1753|On the Motion to Proceed PN1753|Daniel J. Santos, of Virginia, to be a Member of the Defense Nuclear Facilities Safety Board for a term expiring October 18, 2017Daniel J. Santos, of Virginia, to be a Member of the Defense Nuclear Facilities Safety Board for a term expiring October 18, 2017113NaNNaN1.0000000.00.00.00.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.00.0000000.00.00.00.00.00.00.00.00.00.00.0
bill.idyeardatebill.strbill.desccongnosponsorscosponsorstw.latent1tw.abortion.and.social.conservatismtw.agriculturetw.banking.and.financetw.civil.rightstw.congress.and.proceduraltw.crimetw.defense.and.foreign.policytw.economytw.educationtw.energytw.environmenttw.fair.electionstw.federal.agencies.and.gov.regulationtw.gunstw.healthcaretw.higher.educationtw.immigrationtw.indian.affairstw.intelligence.and.surveillancetw.labortw.law.courts.and.judgestw.transportationtw.veterans.affairstw.womens.issues
3172108_hconres2720032003-02-11H CON RES 27|On Motion to Suspend the Rules and Agree|Condemning the Selection of Libya to Chair the United Nations Commission on Human Rights, and for other purposesCondemning the Selection of Libya to Chair the United Nations Commission on Human Rights, and for other purposes108cand260cand820|cand1506|cand524|cand726|cand1433|cand591|cand824|cand1260|cand793|cand806|cand1441|cand1280|cand1353|cand1516|cand600|cand841|cand550|cand1314|cand586|cand1529|cand776|cand715|cand1387|cand1477|cand861|cand556|cand1545|cand880|cand882|cand898|cand9140.2265920.0000000.0000000.0000000.5395550.0022410.0001470.2245600.0000190.0000000.0000000.0000000.0000000.0001420.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0067080.0000370.0000000.000000
3173108_hjres1820032003-02-05H J RES 18|On Motion to Recommit with Instructions|Making further continuing appropriations for the fiscal year 2003, and for other purposes.Making further continuing appropriations for the fiscal year 2003, and for other purposes.108cand163NaN0.5103180.0575150.0000140.0000140.0141420.2534460.0458460.0000230.0414830.0000000.0000000.0000110.0000000.0570910.0000000.0000220.0000000.0000000.0000000.0193540.0001520.0000130.0000200.0000220.000061
3174108_hjres1320032003-01-28H J RES 13|Table the Appeal of the Ruling of the Chair|Further Continuing Appropriations for FY 2003Further Continuing Appropriations for FY 2003108cand163NaN0.7942520.0000000.0000260.0000060.0000000.1442780.0000000.0506560.0000440.0000000.0000000.0000190.0000000.0000490.0000000.0000150.0000000.0000000.0000000.0000000.0000000.0000140.0104620.0001790.000000
3175108_hjres220032003-01-16H.J.Res. 2|On the Amendment S.Amdt. 2|Byrd Amendment No. 2; To provide additional funds for certain homeland security measures.Byrd Amendment No. 2; To provide additional funds for certain homeland security measures.108cand163NaN0.2476580.0199990.0147670.0273550.0158240.1162360.0131010.0291880.0529760.0209190.0084150.0130780.0003030.0497720.0001720.0232140.0107530.0012140.0126540.0232850.0160080.0105870.0190720.0021970.003775
3176108_adjourn20032003-01-08ADJOURN|On Motion to Adjourn|NaN108NaNNaN1.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
3177108_hjres120032003-01-08H J RES 1|Table the appeal of the ruling of the Chair|Making further continuing appropriations for the fiscal year 2003, and for other purposesMaking further continuing appropriations for the fiscal year 2003, and for other purposes108cand163NaN0.4876280.0000000.0194900.0000290.0000000.2836270.0000000.0189470.0000260.0000000.0000000.0001430.0000000.0002290.0000000.1135160.0000000.0000000.0000000.0000000.0000000.0000260.0002470.0760930.000000
3178108_hres1420032003-01-08H RES 14|On Ordering the Previous Question|Providing for the Consideration of S. 23, to Provide for 5-Month Extension of the Temporary Extended Unemployment Compensation Act of 2002 and for a Transition Period for Individuals Receiving Compensation when the Program under such Acts EndsProviding for the Consideration of S. 23, to Provide for 5-Month Extension of the Temporary Extended Unemployment Compensation Act of 2002 and for a Transition Period for Individuals Receiving Compensation when the Program under such Acts Ends108cand1484NaN0.0831200.0000000.0000000.0000000.0000000.8654970.0000000.0000000.0513830.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
3179108_hres1520032003-01-08H RES 15|On Ordering the Previous Question|Providing for consideration of H.J. Res. 1 and H.J. Res. 2, joint resolutions making further continuing appropriations for FY 2003Providing for consideration of H.J. Res. 1 and H.J. Res. 2, joint resolutions making further continuing appropriations for FY 2003108cand1280NaN1.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
3180108_s2320032003-01-08S 23|On motion to commit with instructions|To Provide for a 5-Month Extension of the Temporary Extended Unemployment Compensation Act of 2002 and for a Transition Period for Individuals Receiving Compensation when the Program under such Acts EndsTo Provide for a 5-Month Extension of the Temporary Extended Unemployment Compensation Act of 2002 and for a Transition Period for Individuals Receiving Compensation when the Program under such Acts Ends108cand1639cand597|cand441|cand607|cand1640|cand251|cand1635|cand351|cand160|cand80|cand230|cand513|cand1572|cand545|cand575|cand194302|cand408|cand1561|cand16240.9172820.0000000.0000000.0000000.0000000.0001190.0000000.0000000.0825830.0000000.0000000.0000000.0000000.0000160.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
3181108_hres520032003-01-07H RES 5|On motion to commit with instructions|Adopting Rules for the One Hundred Eighth CongressAdopting Rules for the One Hundred Eighth Congress108cand679NaN0.7903920.0000000.0000330.0000000.0000000.1092160.0000000.0381360.0000470.0000000.0000000.0000000.0218390.0002590.0000000.0001330.0000000.0000000.0000000.0182880.0000000.0103120.0000670.0000000.000000